Full text of "International Journal for Computer Science Research"

See other formats

IJCSIS Vol. 10 No. 6, June 2012
ISSN 1947-5500

International Journal of
Computer Science
& Information Security

IJCSIS PUBLICATION 2012

Editorial
Message from Managing Editor

The International Journal of Computer Science and Information Security (IJCSIS) is a well-
established and notable venue for publishing high quality research papers as recognised by
various universities and international professional bodies. IJCSIS is a refereed open access
international journal for publishing scientific papers in all areas of computer science research.
IJCSIS publishes original research works and reviewed articles in all areas of computer science
including emerging topics like cloud computing, software development etc. The journal promotes
insight and understanding of the state of the art and trends in computing technology and
applications.

IJCSIS solicits authors/researchers/scholars to contribute to the journal by submitting articles that
illustrate research results, projects, surveying works and industrial experiences. IJCSIS helps
academia promptly publish academic work to sustain or further one's career.

For complete details about IJCSIS archives publications, abstracting/indexing, editorial board and
other important information, please refer to IJCSIS homepage. IJCSIS appreciates all the insights
and advice from authors/readers and reviewers. Indexed by the following International Agencies
and institutions: Google Scholar, Bielefeld Academic Search Engine (BASE), CiteSeerX, SCIRUS,
Cornell's University Library El, Scopus, DBLP, DOI, ProQuest. Average acceptance for the period
January-June 2012 is 25-30%.

We look forward to receive your valuable papers. The topics covered by this journal are diverse.
(See monthly Call for Papers). If you have further questions please do not hesitate to contact us
at Hcsiseditor@qmail. com . Our team is committed to provide a quick and supportive service
throughout the publication process.

A complete list of journals can be found at:

http:/ / sites.qooqle.com/ site/ iicsis/

IJCSIS Vol. 10, No. 6, June 2012 Edition
ISSN 1947-5500 © IJCSIS, USA.

Journal Indexed by (among others):

ScieniifcCQmmQns

Google scholar = rit^eer* &UIIUO

i-v^A I directory of INDEX fj^ COPERNICUS rOIJUCSt

l /\ OPEN ACCESS V \2S --— — ~~ ^ aP~~~

\-JKJf-\U JOURNALS INTERNATIONAL M

IJCSIS EDITORIAL BOARD

Dr. Yong Li

School of Electronic and Information Engineering, Beijing Jiaotong University,
P. R. China

Prof. Hamid Reza Naji

Department of Computer Enigneering, Shahid Beheshti University, Tehran, Iran

Dr. Sanjayjasola

Professor and Dean, School of Information and Communication Technology,
Gautam Buddha University

Dr Riktesh Srivastava

Assistant Professor, Information Systems, Skyline University College, University
City of Sharjah, Sharjah, PO 1797, UAE

Dr. Siddhivinayak Kulkarni

University of Ballarat, Ballarat, Victoria, Australia

Professor (Dr) Mokhtar Beldjehem

Sainte-Anne University, Halifax, NS, Canada

Dr. Alex Pappachen J ames (Research Fellow)

Queensland Micro-nanotechnology center, Griffith University, Australia

Dr. T. C. Manjunath

HKBK College of Engg., Bangalore, India.

Prof. Elboukhari Mohamed

Department of Computer Science,
University Mohammed First, Oujda, Morocco

ISSN (online): 1947-5500

Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish
original scientific results.

CALL FOR PAPERS International Journal of Computer Science (IJCSIS)
August-December 2012 Issues

The topics suggested by this issue can be discussed in term of concepts, surveys, state of the art, research,
standards, implementations, running experiments, applications, and industrial case studies. Authors are invited
to submit complete unpublished papers, which are not under review in any other conference or journal in the
following, but not limited to, topic areas.
See authors guide for manuscript preparation and submission guidelines.

Indexed by Google Scholar, DBLP, CiteSeerX, Directory for Open Access Journal (DOAJ), Bielefeld
Academic Search Engine (BASE), SCIRUS, Cornell University Library, ScientificCommons, EBSCO,
ProQuest and more.

Deadline: see web site

Notification: see web site

Revision: see web site

Publication: see web site

Context-aware systems

Networking technologies

Security in network, systems, and applications

Evolutionary computation

Industrial systems

Evolutionary computation

Autonomic and autonomous systems

Bio-technologies

Knowledge data systems

Mobile and distance education

Intelligent techniques, logics and systems

Knowledge processing

Information technologies

Internet and web technologies

Digital information processing

Cognitive science and knowledge

Agent-based systems

Mobility and multimedia systems

Systems performance

Networking and telecommunications

Software development and deployment

Knowledge virtualization

Systems and networks on the chip

Knowledge for global defense

Information Systems [IS]

IPv6 Today - Technology and deployment

Modeling

Software Engineering

Optimization

Complexity

Natural Language Processing

Speech Synthesis

Data Mining

For more topics, please see web site https://sites.google.com/site/ijcsis/

rirXlv.org Google scholar

scirus

JtvpttfoCem mom j k C^k-H .dOCSt&C

— <= ' JJl_MLAJ find and share profs

professions documents

^BABE

BialuhA) Acattamc Search Ln$ k

CiteSeer*™

.uni-trier. de

Computer Science

Bibliography

DOA J s ™

LJ\Jf- \_l joupi

RECJfORYOF
ACCESS

PNAL5

ProQuest

For more information, please visit the journal website (https://sites.google.com/site/ijcsis/)

TABLE OF CONTENTS

1. Paper 20051207: Comparison of Data Mining Techniques Used To Predict Cancer Survivability (pp. 1-6)

Charles Edeki \ Shardul Pandya, Ph.D. 2 ,

1 Mercy College, Mathematics and Computer Science Department, 555 Broadway, Dobbs Ferry, NY 10522

2 School of Business and Technology, Faculty of Information Technology, Capella University, 225 South Sixth
Street, Minneapolis, Minnesota - US

2. Paper 30051217: Advanced Security-Based Data Sharing Model for the Cloud Service Providers (pp. 7-12)

Mohamed Meky and Amjad Ali

Center of Security Studies, University of Maryland University College, Adelphi, Maryland, USA

3. Paper 31051232: Secure And Context-Aware Routing In Mobile Ad-Hoc Networks (pp. 13-18)

R. Haboub And M. Ouzzif

RITM laboratory, Computer science and Networks team, ESTC - ENSEM - UH2 Casablanca, Morocco

4. Paper 31051243: Non-Linear Attitude Simulator of LEO Spacecraft and Large Angle Attitude Maneuvers
(pp. 19-25)

Azza El-S. Ibrahim, Electronics Research Institute: Computers & Systems dept., Giza, Egypt

Ahamed M. Tobal, Electronics Research Institute: Computers & Systems dept, Giza, Egypt

Mohammad A. Sultan, Cairo University, Faculty of Engineering: Electronics & Communications dept, Giza, Egypt

5. Paper 31051254: The Evaluation of Performance in Flow Label and Non Flow Label Approach based on
IPv6 technology (pp. 26-29)

Nevila Xoxa Resulaj, Albanian Academy of Science, Tirane, Albania

Nevila Baqi Kadzadej, University of Tirana, Faculty of Economic, Mathematics, Statistics and Applied Informatics

Department, Tirana, Albania

Igli Tafa, Polytechnic University of Tirana Faculty of Information Technology, Computer Engineering Department,

Tirana, Albania

6. Paper 31051256: False Colour Composite Combination Based on the Determinant of Eigen Matrix (pp. 30-
32)

Maha Abdul-Rhman Hasso

Department of Computer Science, College of Computer Sciences and Math., University of Mosul / Mosul, Iraq

7. Paper 30041214: Source Initiated Energy Efficient Scheme for Mobile Ad Hoc Networks (pp. 33-40)

R. Bhuvaneswari, Anna University of Technology, Coimbatore, India

Dr. M. Viswanathan, Fluid Control Research Institute (FCRI), Palakkad, Kerala, India

8. Paper 31051227: Assesment of Cobit Maturity Level With Existing Conditions From Auditor (pp. 41-49)

/ Made Sukarsa, Maria Yulita Putu Dita, I Ketut Adi Purnawan

Faculty of Engineering, Information Technology Studies Program, Udayana University, Kampus Bukit Jimbaran,

Bali, Indonesia

9. Paper 30051219: Intrusion Detection and Prevention Response based on Signature-Based and Anomaly-
Based: Investigation Study (pp. 50-56)

Dr. Homam ElJTaj, Fahad Bin Sultan University, Tabuk, Saudi Arabia
Firas Najjar, VTECH - LTD. Riyadh, Saudi Arabia.
Hiba Alsenawi, Fahad Bin Sultan University, Tabuk, Saudi Arabia
Dr. Mohannad Najjar, Tabuk University, Tabuk, Saudi Arabia

10. Paper 31051233: Extended Sakai-Kasahara Identity-Based Encryption Scheme to Signcryption Scheme
(pp. 57-60)

Hussein Khalid Abd-Alrazzaq, College of Administration and Economic-Ramadi, Anbar University, Anbar, Iraq

11. Paper 31051236: Multi-Pixel Steganography (pp. 61-66)

Dr. R. Sridevi, Department of Computer Science & Engineering, JNTUH College of Engineering, Hyderabad, A.P.,

India

G. John Babu, Department of Computer Science & Engineering, Sreekavitha Engineering College

Khammam- A.P. - India

12. Paper 31051248: Design of 16 bit Low Power Processor (pp. 67-71)

Prof. Khaja Mujeebuddin Quadry, Royal Institute of Technology & Science, Chevella, R. R. Dist. A. P. India
Dr. Syed Abdul Sattar, Professor & Dean of Academics, Royal Institute of Technology & Science, Chevella, R. R.
Dist. A. P. India.

13. Paper 31051251: Integration of Floating Point Arithmetic User Library to Resource Library of the CAD
Tool for Customization (pp. 72-76)

R. Prakash Rao, St. Peter's Engineering College, Maisammaguda, Hyderabad, India

Dr. B. K. Madhavi, Geetanjali College of Engineering & Technolog, Cheryala, Hyderabad, India

14. Paper 18091102: V-Diagnostic: A Data Mining System For Human Immuno- Deficiency Virus Diagnosis
(pp. 77-81)

Omowunmi O. Adeyemo, Adenike O. Osofisan

Department of Computer Science, University oflbadan, Ibadan, Nigeria

15. Paper 31051229: A Web-Based System To Enchance The Management Of Acquired Immunodeficiency
Syndrome (AIDS)/ Human Immunodeficiency Virus (HIV) In Nigeria (pp. 82-89)

1 Agbelusi Olutola, 2 Makinde O.E, 3 Aladesote O. Isaiah, and 4 Aliu, A. Hassan

1 &3 Computer Science Department, Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria.

2 Ajayi Crowther University, Oyo, Oyo State, Nigeria

4 Mathematics & Statistics Department, Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria

16. Paper 31081151: Data Mining System For Quality Prediction Of Petrol Using Artificial Neural Network
(pp. 90-95)

Omowumi O. Adeyemo, Adenike O. Osofisan, Ebunoluwa P. Fashina, Kayode Otubu
Department of Computer Science, University oflbadan, Ibadan, Nigeria

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Comparison of Data Mining Techniques used to
Predict Cancer Survivability

Charles Edeki, Ph.D

Mathematics and Computer Science Department

Mercy College

Dobbs Ferry, NY, USA

cedeki @ mercyma vericks . edu

Shardul Pandya, Ph.D

School of Business and Technology, Faculty of

Information technology

Capella University

Minneapolis, MN, USA

Shardul.Pandya@capella.edu

Abstract — Huge efforts are being made by computer
scientists and statisticians to design and implement
algorithms and techniques for efficient storage,
management, processing, and analysis of biological
databases. The data mining and statistical learning
techniques are commonly used to discover consistent and
useful patterns in a biological dataset. These techniques
are used in a computational biology and bioinformatics
fields. Computational biology and bioinformatics seeks to
solve biological problems by combining aspects of biology,
computer science, mathematics, and other disciplines [1].
The main focus of this study was to expand understanding
of how biologists, medical practitioners and scientists
would benefit from data mining and statistical learning
techniques in prediction of breast cancer survivability and
prognosis using R statistical computing tool and Weka
machine learning tool (freely available open source
software applications). Six data mining and statistical
learning techniques were applied to breast cancer datasets
for survival analysis. The results were mixed as to which
algorithm is the most optimal model, and it appeared that
the performance of each algorithm depends on the size,
high dimensionality of data representation and cleanliness
of the dataset.

Keywords- Data Mining, WEKA, R tool, Computational
Biology, Bioinformatics

I. Introduction
The advancement of medicine now relies upon the
collection, management, storage, and analysis of large
biological datasets. Data mining, statistical and machine
learning techniques are the process by which new knowledge
is extracted from a dataset. According to [2], the definition of
machine learning is as follows: 'A computer program is said

to learn from experience E with respect to some class of tasks
T and performance measure P, if its performance at tasks in T,
as measure by P, improves with experience E" (p. 2). Data
mining, statistical and machine learning are based on inductive
inference, a process of observing a phenomenon, then building
a model for that phenomenon and making predictions using
the model.

In this study, the results of a comprehensive comparative
study of the following data mining, statistical and machine
learning algorithms was examined:, Support Vector Machines
(SVM);, RandomForest;, AdaBoost, Bagging;, Boosting;,
Decision Trees and Artificial Neural Networks (ANN)
classifiers algorithms. The main focus of this research was to
study the effective classification learning techniques for
prediction of breast cancer survivability. In other words, can
one algorithm or techniques be more effective at predicting
survivability over others.

There are two main aspects in prediction of cancer
survivability: accuracy (how true is the algorithm's
prediction), and efficiency (how fast can the algorithm execute
the prediction task). Data reduction technique was applied to
the dataset and obtained a reduced representation of the breast
cancer dataset. The resulting data set was much smaller in
volume, yet closely maintained the originality of the data [3].
The R PCA function was used to reduce the large dataset (the
patients in this case) to smaller components of objects related
according to their expression patterns with tumor size.

Classification algorithms are the most common data mining
and machine learning algorithms, often used for data analysis
in both industry and academia. Classification is a supervised
learning algorithm used to map a dataset into predefined
groups or classes. The biological datasets from the National
Cancer Institute (NCI) biological database system was used to
find the prediction rate of each algorithm and comparative

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

studies of the algorithms were performed in order to find the
optimal classification model.

R and Weka software were used to analyze the breast cancer
dataset. R is open source statistical analysis software and
Weka is open source machine learning application software
that can be used to normalize and analyze datasets.

II. METHODS

The exponential growth of the amount of biological
data available raises two problems: on one hand, efficient
information storage and management, and on the other hand,
the extraction of useful information from these data. The
second problem is one of the main challenges in computational
biology, which requires the development of an effective
computational analysis tool and is the problem that was
presented in this study.

For many studies in medicine, researchers are
interested in assessing the time it takes for an event to happen.
Very often, the event is an outcome, such as diagnosis or death,
but the outcome may also be other measurable parameters,
such as onset of disease or relapse of disease. There is a term
that describes the period leading to the event, called survival
time. Furthermore, survival analysis is the term used to
describe the investigation into the patterns of these events that
occur within one or more cohorts in a study [4]. In dealing
with the analysis of survival data, researchers are interested in
the length of time it takes a patient to reach an event rather than
simply the fact that the event has or has not occurred.

There are at least two ways to motivate why particular
data mining and statistical learning techniques were suitable for
a particular learning task [5]. One way was through
comparative studies and the other was through benchmarking
[5]. This research study was based on comparative study of
data mining and statistical learning techniques. Each of the data
mining and statistical learning techniques is briefly discussed
below.

Support Vector Machine (SVM) was mainly
developed by Vladimir Vapnik and is based on the structural
risk minimization principle from statistical learning theory.
SVM algorithm uses a nonlinear mapping to transform original
training data into higher dimensions. Then SVM searches for
the linear optimal separating hyperplane within the new
dimension. The hyperplane is the decision boundary separating
the datasets of one class from another. The SVM finds this
decision boundary using training sets or support vectors, and
margins defined by the support vectors. SVM is very accurate
due to its ability to model complex nonlinear decision
boundaries and is, less prone to overfitting problem, but
according to [3], SVM is very slow when compared with other
classification algorithms [3] and [6].

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
The decision tree algorithm is the most popular algorithm
in data mining classification technique because it is easy to
understand how it makes predictions. There are many decision
tree algorithms for constructing a decision tree, such as ID3,
C4.5, SLIQ, Scalable Parallelizable Induction of Decision Tree
(SPRINT), etc. There are two phases in generating or creating
a decision tree, namely the tree-growing phase and tree-pruning
phase. In the tree-growing phase the algorithm starts with the
whole data set at the root node. The data set is partitioned
according to a splitting criterion into subsets. This procedure is
repeated recursively for each subset until each subset contains
only members belonging to the same class or is sufficiently
small. In the tree-pruning phase, the decision tree is reduced in
order to improve time complexity and prevent overfitting [7].

AdaBoost is one of the most powerful learning ideas
introduced in the past twenty years. It was originally designed
for classification problems, but has been extended to regression
as well [9]. AdaBoost is a popular ensemble method and has
been shown to significantly enhance the prediction accuracy of
the base learner [4]. It is a learning algorithm used to generate
multiple classifiers and to utilize them to build the best
classifier [10]. The process of boosting is to combine the
outputs of many weak classifiers to produce a powerful
classifier. The predictions from the weak classifiers are then
combined through a weighted majority vote to produce the
final prediction [9]. The advantage of this algorithm is that it
requires less input parameters and needs little prior knowledge
about the weak learner [4].

The study of artificial neural networks (ANN) was
inspired by attempts at mimicking the brain functionality [11].
Neural networks represent an alternative computational
paradigm, which has received much attention in the past few
decades [12]. Neural networks are capable of predicting new
classes based on past examples after executing a process of
learning. There are two phases in the processes of training the
artificial neural network: learning and recalling. Networks are
trained by inputting a training dataset with the target data.
Weights are adjusted until the outputs reach the desired training
outputs. The goal is to minimize the error, which is the
difference between the target output and desired output. After
learning, the testing dataset would be applied to the artificial
neural network to estimate the desired output and determine the
performance of learning.

The general approach that was used for predictive
model building in this research is as follows:

1. Create training and testing datasets.

2. Apply a data mining/statistical learning technique
to the training set.

3. Generate the predictive model.

4. Evaluate model using testing dataset.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

5. Repeat step# 2 with other techniques.

6. Compare performance between techniques.

The breast cancer dataset consists of five categories of
patient data, as shown in Table 1, that exist for more than
62,000 breast cancer patients diagnosed in the United States
between 1990 and 1997. Thus, all files contain variable data
for the same group of patients. The dataset originated from
The Surveillance, Epidemiology, and End Results (SEER)
Program of the NCI. Most of the data, including pathology,
diagnosis, and treatment, are real and excellent biomedical
dataset. The demographic data, however, was partially
artificial due to patient's privacy, as the original dataset from
SEER is completely anonymous. This identifier acts like a
hospital record number of a patient but is purely fictitious, as
the original data is anonymous. Variables for the complete
patient dataset are shown in Table 1 .

There are a number of methods that can be used to
transform data variables into forms that are usable by data
mining algorithms. The Weka data-mining tool was used for
the preparation of the breast cancer datasets for mining.

The PCA data reduction method (prcomp( ) function)
in R statistical program was used to reduce the dataset. PCA is
a statistical method routinely used to analyze interrelationships
within a large set of data, revealing common underlying factors
or components. PCA examines the correlations between the
original data values and condenses the information contained
within objects into smaller group of components with minimal
loss of information.

According to [4], stratified 10-fold cross-validation is
a common validation method used to minimize bias and
variance associated with random sampling of the training and
test datasets. Also, it is a common method for data selection in
machine learning related to medical and biological research.
The stratified 10-fold cross-validation process was used in this
study in evaluating and validating the predictive model. The
process consists of four steps as follow [4]:

1 . Divide the dataset into a set of subclasses.

2. Assign a new sequence number to each set of
subclasses.

3. Randomly partition the subclass into 10 subsets
or folds.

4. Combine each fold of each subclass into a single
fold.

The Weka data mining tools support automatic
splitting of a data set into training and test sets using either a
straight percentage splits or through k-fold cross validation.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
TABLE 1 . PATIENT DATSET VARIABLES

Table Name

Attribute Name

Attribute Description

Demographic

patientid

unique patient identifier

data

dateofbirth

(artificial)

maritalstatus

patient date of birth (artificial)

race

marital status at diagnosis

ageatdiagnosis

patient ethnicity

alivestatus

age at diagnosis

survival time

patient alive or dead
survival time from date of
diagnosis

Diagnosis

patientid

data

yearofdiagnosis

year of diagnosis

histology

histologic type of tumor

primarysite

site of primary tumor

numberofprimaries

number of primary tumor

Pathology

patientid

data

Grade

tumor grade

Nodesexam

number of lymph nodes

Nodespos

examined

Extent

number of positive lymph nodes

Nodalstatus

extent of disease

Size

status of lymph node

Pgr

involvement

size of tumor

progesterone receptor status
estrogen receptor status

Staging

patientid

Stage

stage of tumor

Treatment

patientid

Surgery

surgery regime received

Radiotherapy

radiotherapy received

III.

RESULT

This section discusses analysis of the breast cancer dataset
by various methods.

Analysis was begun by performing logistic regression on the
complete 10-year survival dataset. The summary( ) function
was used and length on the alivestatus factor to determine the
number of rows for each outcome, as well as the total number
of patients as shown in Table 2.

TABLE 2. TOTAL NUMBER OF PATIENTS

Total Number of Rows in the Dataset

Number of live patients 11,714

Number of dead patients 3,480

Total number of patients 1 5 , 1 94

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

The number of patients alive after 10 years (row 0) is more
than three times the number of patients that have died (row 1).
To create a logistic regression model, glm( ) function is called,
which provides a model that is an equation to predict whether a
patient will survive 10 years. To evaluate the predictive ability
of the model, we used the predict( ) function to predict the
probability of outcome for all cases in the dataset. The
classification result of the logistic regression was 12,080
(11,301 + 779) correct predictions (true positive and true
negative), and 3,114 (2,697 + 417) incorrect predictions,
resulting in the overall accuracy of 79.5% (12,080/15,194).
The precision was 80.7% (11,301/13,998). The recall was
96.4% (11,301/(11,301+417)).

Logistics Regression with Holdout: We repeated the logistic
regression approach using the holdout method that contained
lesser dataset to evaluate the model; the result was 1,482 (816
+ 666) correct predictions (true positive and true negative), and
604 (392 +212) incorrect predictions, resulting in the overall
accuracy of 71% (1,482/2,086). The precision was 67.5%
(816/(816+392)). The recall was 79.4% (816/(816+212)).

Decision Tree Algorithm: The Weka's J48 decision tree
learner, based on C4.5 decision tree algorithm was used with
default parameter setting to build a decision tree model for a
10-year survival dataset. The function was called J48 and is
already implemented in RWeka. The precision for the model is
79.9% (2,485/(2,485+631)). The decision tree model was
evaluated using the 10-fold cross-validation.

The multilayer perceptron learner algorithm in Weka with
default parameter settings was modified such that it could serve
as a Neural Network. The hidden layers parameter was set to
one hidden layer with five nodes to build the artificial neural
network model for a 10-year survival dataset. The function is
called multilayerPerceptron and is already implemented in
RWeka. The model was evaluated using 10-fold cross-
validation and the original train. full_l dataset was used to build
the model. The result was 72.94% accuracy in classification.
The correct prediction was 4926/(4926+1827), which was
72.94% and incorrect prediction was 1827/(4926+1827), which
was 27.1%. The kappa statistics was 0.523.

The next modeling approach was a support vector machine
(SVM). The SVM algorithm implemented in Weka is called
SMO (sequential minimal optimization). A significant factor
in the SVM model-building process is parameter adjustment.
The SVM model was generated using RWeka's built-in
function, SMO( ). Ten-fold cross validation of the SVM model
was performed and the model was evaluated using the 200-
instance test set.

The SVM model accuracy result on the full dataset was
68.4%, the correct prediction was 4620/(4620+2133), and

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
incorrect prediction was 2133/(4620+2133), which was 31.6%.
The kappa statistics was 0.3683 and the ROC area was 0.684.

We applied boosting to the breast cancer dataset using J48
decision tree as our model-building algorithm. To implement
AdaBoost.Ml, we called the AdaBoostMl( ) function and set
the classifier algorithm parameter (W) to "J48" using
Weka_control().

We evaluated the model by performing 10-fold cross-
validation; the boosted model is then evaluated on the small
test set. The boosting model accuracy result on the full dataset
was 69.5%, the correct prediction was 4694/(4694+2059) and
incorrect prediction was 2059/(4694+2059), which was 30.5%.
The kappa statistics was 0.3902 and the ROC area was 0.759.
The boosting model accuracy result on the 200_test data was
73%. We applied bagging to the breast cancer dataset using the
J48 decision tree. The bagging( ) function in Weka was called
and set the classifier algorithm parameter (W) to "J48". The
model was evaluated by performing 10-fold cross-validation,
the bagged model was evaluated on the small test set (200
instances).

The bagging model accuracy result on the full dataset was
68.84%, the correct prediction was 4649/(4649+2104), which
was 68.84% and incorrect prediction was 2104/(4649+2104),
which was 31.16%.

The RandomForest model was built using Weka's
RandomForest ( ) function, which is based on the same concept
as the original Random Forest algorithm developed by Breiman
(Breiman, 2001). Like boosting and bagging, the Random
Forest model was created using the Weka's RandomForest( )
classifier and evaluated the model by performing 10-fold cross-
validation. Using Weka_control() function, the RandomForest(
) function created 1,000 trees by setting the parameter I to
1000.

The Random Forest model accuracy result on the full
dataset was 75%, the correct prediction was 5064/(5064+1689),
which was 74.99% and incorrect prediction was
1689/(5064+1689), which was 20.01%.

The summary of the prediction results of the data mining
and statistical learning algorithms are shown in Table 3. The
SVM classifier is the only algorithm that did not improve when
applied to the independent dataset with 200 records. The rest
of the algorithms showed slight improvement when applied to
the independent dataset.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

TABLE3. PREDICTION RESULTS OF THE ALGORITHMS

Type

Overall

Precision

Precision -

Accuracy

Accuracy —

-full

200

-Full

200

dataset

Independent

Dataset

Independent
dataset

dataset

Logistics Regression

71%

72.5%

67.5%

68.3%

Decision Tree - J48

70.17%

71.5%

71.7%

74.2%

ANN

72.94%

73.04%

74%

74.7%

MultilayerPerceptron(

) function

Support Vector

68.414%

66.5%

69.7%

69.4%

Machine (SVM)

using Weka's

Sequential Minimal

Optimization (SMO)

Boosting-

69.5%

73%

70.2%

71.7%

AdaBoostMl

Bagging - Weka's

68.84%

72%

67.3%

71.6%

Bagging( ) function

Random Forest -

75%

76.6%

72%

73.1%

Weka's

RandomForest

function

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
tree, SVM, AdaBoost, Bagging and naive Bayes algorithms
based on accuracy. However, the artificial neural network
showed slight improvement over logistic regression, while the
decision tree resulted in slightly higher classification accuracy
over AdaBoost, Bagging and naive Bayes' models in terms of
accuracy. The outcome of this study indicated that data mining
and statistical learning are not necessarily a panacea to
understanding the prediction and diagnostics of breast cancer
problem. It is hoped that this study advances the understanding
of the appropriateness and effectiveness of selecting
appropriate data mining, machine learning and statistical
learning methodologies in prediction of breast cancer
survivability. Results indicate that in terms of accuracy and
precision, Random Forest and Artificial Neural Network
techniques are the good models to use for prediction of breast
cancer survivability. Improving the prediction's accuracy and
precision rates is possible by actions that include changing the
size of the variables, reducing the feature or selecting most
reliable features using PCA, singular value decomposition
(SVD), RELIEF or any robust feature selection algorithm. The
modification of data preprocessing techniques, adjusting
runtime parameters, and generating ensemble methods with
different parameters may improve the precision and accuracy
rates.

IV. DISCUSSION

The prediction of cancer survivability has been a major
issue in medicine and biology. In this study, we have explored
six different statistical and machine learning methods for
generating predictive models for datasets with either binary or
continuous response variables. It is critical that one does not
apply classification or regression methods to datasets without
having confidence that the methods are indeed suitable for data.

For the binary outcome survival status dataset, we
generated six models from diverse statistical learning and data
mining techniques. This was useful because it gave us a choice
of models and indicated which model is superior by assessing
the accuracy and precision. From the accuracy perspective, the
best model is RandomForest (75.0%). We did, however,
express concern about cost of predicting patients to survive 10
years but who actually die (False-Negative). If this is more
important than overall accuracy or precision, our best model is
produced by bagging (26.5% error) and the worst is the
decision tree (33.3% error). The second best error rate for
false-positive is Random Forest (30% error). Clearly there is
much to think about even after we have generated the models,
from this study, we can say the result of each model depends
on the quality of the biological dataset, the size of the dataset
and the representation of the dataset.

Results of the classifiers applied to the full breast cancer
dataset were mixed. Logistic regression outperformed decision

V. CONCLUSION

Medical institutions looking to undertake a data mining
approach to solve biological problems could be well-served by
including statistical learning and data mining processes in their
analytical and intervention efforts. Computer scientists,
medical researchers and statisticians need to look at their own
biological data availability for variables that might potentially
link to prediction of cancer survivability. The selection of
variables in this study was based on computational biology and
bioinformatics literatures, breast cancer dataset available and
domain knowledge of the researcher.

Data preparation (data quality) could be the difference
between a successful machine learning project and a failure and
takes between 60-80% of the whole data mining or machine
learning effort or process (Witten & Frank, 2005).

The ability of a medical practitioner to effectively pinpoint
how long cancer patients survived during treatment, may lead
to better evaluation of the treatment and design of personal
medicine or drugs to breast cancer patients. This study provides
a detailed account of a data mining process applied to the
prediction of breast cancer survivability. The data mining
process followed for this study began with the inclusion of a
large set of factors that was reduced manually through standard
statistical analysis.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

Findings indicate that none of the data mining and
statistical learning algorithms applied to the breast cancer
dataset outperformed the others in such a way that it could be
declared the optimal algorithm. Additionally, none of the
algorithm performed poorly as to be eliminated from future
prediction model in breast cancer survivability tasks.

Acknowledgment

We would like to extend our appreciation and thanks to Dr.
Eugene Fink at Carnegie Mellon University and Dr. John
Rusnak at Capella University for their advice and
recommendations in this study. The data used in this study is
freely available at National Cancer Institute website; the Weka
and R were free software applications that were available to
download online.

References

[1] J. Adams, S. Matheson, and R. Pruim, BLASTED:
Integrating biology and computation,

Journal of Computing Sciences, vol. 24, pp. 47 54,
2008.
[2] T.Mitchell, Machine Learning, San Francisco, CA:

McGraw Hill 1997.
[3] J. Han, and M. Kamber, Data Mining: Concepts and

Techniques, San Francisco, CA: Morgan Kaufman,

2008.
[4] J. Thongkam, G. Xu, Y. Zhang, and F. Huang,

"Breast cancer survivability via AdaBooost

Algorithms", Australian workshop on health data

and knowledge management, Wollongong, NSW,

Australia, 2007.
[5] T. Joachims, "A Statistical learning model of text

classification for support vector machines", SIGIR, New

Orleans, LA, 2001.
[6] V. N. Vapnik, Statistical Learning Theory,

Chichesrter, GB: Wiley, 1998.
[7] C. Kleissner, Data mining for the enterprise.
"1060-3425/98 IEEE". Retrieved from the

Institute of Electrical and Electronics Engineers

(IEEE) Digital Library.
[8] K .Sattler, and O. Dunemann, "SQL database primitives

for decision tree classifiers", Proceedings of the 2001

CIKM Conference. Atlanta, Georgia, 2001.
[9] T. Hastie, R., Tibshirani, and J. Friedman, The Elements

of Statistical Learning: Data Mining, Inference and

Prediction. New York, NY: Springer, 2001.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
[10] R. E. Schapire, and Y. Singer, "Improved boosting

algorithms using confidence-rated predictions", Journal
of Machine learning, vol. 37, pp. 297-336, 1999.
[11] P. Tan, M. Steinbach, and V. Kumar,

Introduction to Data Mining. Boston, MA:
Addison Wesley, 2006.
[12] J. Hertz, A. Krogh, and R. Palmer, Introduction to the
heory of Neural Computation. New York, NY:
Addison-Wesley.
[13]. J. H. Witten, and E. Frank, Data Mining: Practical
Machine Learning Tools and Techniques. San
Francisco, CA: Morgan Kaufman.

AUTHORS PROFILE

Charles Edeki, Ph.D.

Faculty Member

Mercy College, Mathematics and Computer Science

Department,

555 Broadway, Dobbs Ferry, NY 10522

cedeki @ mercymavericks.edu

Phone: 917-627-0024

Shardul Pandya, Ph.D.

Information Technology, Information Management Professor,

Dissertation Supervisor/Mentor

Research Methods Faculty

School of Business and Technology,

Faculty of Information Technology,

Capella University,

225 South Sixth Street,

Minneapolis, Minnesota - US

Shardul.Pandya@Capella.edu

Phone: 804-337-3445

Fax: 804-835-6447

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Advanced Security-Based Data Sharing Model for the

Cloud Service Providers

Mohamed Meky

Center of Security Studies

University of Maryland University College

Adelphi, Maryland, USA

mmeky@faculty.umuc.edu

Amjad AH

Center of Security Studies

University of Maryland University College

Adelphi, Maryland, USA

amjad.ali@umuc.edu

Abstract- The authors recently published a security model that
provides data owner full control over data sharing in the cloud
environment and prevents cloud providers from revealing data
to unauthorized users. Security analysis has demonstrated that
the published model meets cloud security requirements and is
resilient to several security threats. However, in the subject
published model, the cloud service provider was a passive
party that did not have the authority to authenticate nor
confirm users' access policies before forwarding encrypted
data to authorized users. In this paper, the authors propose an
enhanced model that introduces authentication and policy
confirmation authorization to cloud service providers without
compromising the full data owner control. The result is an
advanced security-based data sharing model that may be
applied to secure data sharing of highly sensitive information
in the cloud environment.

Keywords- cloud computing; cloud storage; data sharing
model; data access control; data owner full control, cloud storage
as a service; data encryption

Introduction

Cloud computing offers new service opportunities with
more efficient resource utilization, on-demand scalability,
and cost reduction for organizations. An enterprise could use
on-demand cloud computing services to increase its storage
capacity or add capabilities to their infrastructure or
applications without the need to acquire new hardware
licenses for new software and the necessary training [1].
Cloud computing services include three major models:
Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS),
and Infrastructure-as-a-Service (IaaS) [2]. Examples for
SaaS, PaaS, and IaaS are Salesforce [3], Google App Engine
[4], and Amazon S3 [5] respectively. However, adapting
cloud computing services introduces several security threats
to data, as data is no longer on the data owner's premise and
cloud providers may have complete control on the computing
infrastructure that underpins the services. Some of these
security threats include unauthorized data access,
compromised data integrity and confidentiality, and lack of
full owner control over data. With the cloud computing
industry still being defined, it is often unclear which party is
responsible for security-related issues. In addition, the
customer ignorance of security practices and service
providers' refusal to release information is ranked among the

top security risks in cloud computing [6]. As a result, it is
vital to question cloud service providers about security-
related concerns prior to migrating customer data. The
increasing concerns about the data security threats in the
cloud environment has prompted several research efforts
such as [7-9] to establish data sharing models that mitigate
the potential security risks while allowing data owners to
continue enjoying the enormous benefits cloud computing
offers. However, these research efforts are either applicable
to specific data format or encryption technique. In addition,
they don't provide full control for data owners to grant/deny
access to data sharing to authorized users. The authors
analyzed the previous research efforts and published a model
that allows the data owners to have full control over granting
or denying access to data sharing in the cloud environment.
However, the cloud provider was a passive party in the
published model and did not have the authority to
authenticate nor confirm users' access policies before
forwarding encrypted data to authorized users. This
advanced security-based data sharing model for cloud
providers will provide additional security layer to ensure
security of highly sensitive information for military and
intelligence organizations in the cloud environment by
synchronizing security controls between data owners and
cloud service providers. The remainder of this paper is
organized as follow. Section II describes the details of the
advanced security-based model. Section III explains the
security analysis of the advanced security-based model, and
finally, section IV concludes the paper.

II. THE ADVANCED SECURITY-BASED MODEL

The mechanism of the advanced security-based model is
illustrated based on a scenario in Figure 1 and notations
listed in Table 1. As shown in the advanced security -based
model in Figure 1, a data owner receives a data access
request message (mi) from a user (step 1). Then, after
successfully authenticating the user's identity, the data
owner simultaneously issues an access ticket (step 2) to the
user and a permit ticket (step 3) to the cloud service
provider. The access ticket contains a control message (ra 2 )
and an access certificate (raj). The user forwards the access
certificate, issued by the data owner, to the cloud provider
(m 5 ) as shown in step 4. Then the cloud provider compares
the permit ticket (m 4 ), issued by the data owner, and the

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

access certificate (m 5 ) submitted by the user. If there is a
match, the cloud provider ensures that the access certificate
is authentic and grants data access to the user. The user
decrypts and authenticates the data retrieved from the cloud
provider through control information (m 2 ).

Fro.

Vtaai
D Ha Dirt-ir

it-id

D-tD
V-tD

m j

- v j

0-/D
■V

» (Step 4)
Present Atess
Certificate

User

IhiP

V^ Provider J
/ (Step 5) \

1 ill !']■«■ 1 I"l.|- 1

EHC fdetaj

(StepL)

Data Access Ri
m

S>i -

Step 3>

Fruht

,k*i 5J

r-,

Data Owner

(Step 2)
Atxtit Ticket
iC'0B1*»l Mmi(( * Act* is Certificate)

Figure 1 . Advanced security-based data sharing model for cloud
service providers

TABLE I.

MODEL'S Notations

Notation

Description

Comments

O-ID

Data Owner ID

C-ID

Cloud storage provider ID

U-ID

User ID

D-ID

Shared data ID

User secret anonymity

Published by data
owner

Cloud provider secret anonymity

Published by data
owner

Secret encryption key for exchanging
messages between data owner and the
user

Published by data
owner

Secret encryption key for exchanging
messages between data owner and the
cloud provider

Published by data
owner

XOR

Logical exclusive or operation

A one-time session key to be used
with XOR operation when transferring
message from the cloud provider to
the user

Generated by data
owner

h(.)

A one-way secure hash function such
as SHA-1

A concatenation operator

l-h

Encryption operator using encryption
key, k

Encryption algorithm used for
encrypting the shared data

Chosen by the
data owner based
on data type

ENC(data)

Encrypted data

Sent by cloud
provider

Encryption key used for encrypting
the shared data

Chosen by the
data owner

h(data)

Hash value of the shared data

Calculated at the
data owner

Unlike in the previously published model [10] where a
cloud service provider has a passive role in authenticating
data users, the proposed advanced security-based model
introduces a new and enhanced capability for cloud provider
to authenticate users and confirm their policies before
forwarding them the encrypted data. This new capability is
achieved by a permit ticket, sent by the data owner to the
cloud service provider, and the access certificate, sent by
user to cloud provider, as shown in Figure 2.

From
DalJ Ov.i-nr

D-tO

n-m

l-ID

L-ID

■\,

■v«

N i

From

Ittr

Submit the ucess certificate ah
within (he access ticket ttir

Data Owner

Access Certificate

f.j

User

Figure 2. User's authentication and policy confirmation capability of the
cloud provider

To execute the proposed advanced security-based model,
data owner first needs to complete the following
prerequisite tasks:

a) Issue two secret anonymities, SC and SU, for the cloud
service provider and the user.

b) Issue two secret symmetric encryption keys, dc and du, for
the cloud service provider and the user.

c) Use a secure channel, such as Diffie-Hellman key
agreement [11], to exchange SC and dc with the cloud
provider, and submit SU and du to the user

After completing the above prerequisite tasks, the
proposed advanced security-based model follows the below
six steps:

1. A user requests data access from the data owner

A user who would like to access data, defined by D-ID,
generates a nonce, N u , and prepares a message mj= (O-ID //
D-ID // NJ to be sent to the data owner. The user then sends
a data access request message = (U-ID, (mj // h (mj //
SU)}du\ to the data owner as shown in Figure 3.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

l^s"! 1- Request Data Access | Dan uuner. |

?KVm "> " ™ ~^7Z~, P^e: ... .n* m

Clcwd Provider

2- Send Access Ticket

<i —

"l_, - \C-ID f/D-iD //.V //.V I!

i04D,li,tttm s l iftfrn./ll"}) tisi'ft !

prepare: m j
lR t ={i:-IB//It-at//NJ/N Sits sop)

4- Send Permit Ticket
G>

prepare m =

W-JW

I 4D

lO^>,lm 4 ll kf^mj ommtV*m

5- Present Access Certificate

{L.ID,//lifm //ks}}

6- Send lire Encrypted Data

ipi ! nyc uiiihi) }.xox to

it-ID

V-tD

Figure 3. Exchange of messages among user, data owner, and cloud
provider

2. Data owner authenticates and sends access ticket
to the user

Upon receiving the data access request from the user,
the data owner executes the following tasks:

a) Decrypt the received message, using the symmetric
secret key, du, (that is relevant to U-ID) and obtain
m } = (O-ID, D-ID//NJ and h (m, // SU).

b) Verify the format of O-ID, D-ID from the decrypted
message m y . If there is no match, the data owner
terminates the connection. Otherwise, the data
owner continues.

c) Compute h (mj // SU) and check whether it equals
the received h (m, // SU). If there is a match, the
data owner determines the authenticity of the user.

After authenticating the user, the data owner generates a
nonce, Nd, a one-time session key, ks, and prepares the
access ticket to be sent to the user. The access ticket, fO-
ID, {m 2 // {m 3 } dc // h (m 2 // {m 3 j dc // SU)j du j, includes the
control message, m 2 = {C-ID // D-ID // N u //// N d IIEN//kd
// h (data) // ks // OP}, and the access certificate, m 3 = {U-ID
// DID // N u // N d HOP}. The optional field, OP, could be
used to extend the capability of the advanced security-based
model. For example, the optional field could have the time
limits when the data should be accessed or special access
policy that could be related to Mandatory Access Control
(MAC) or Role Based Access Control (RBAC). It is
important to note that the access ticket is encrypted by the
secret key, du, known only to the user. While the access
certificate is encrypted by the secret key, dc, known only to
the cloud provider.

3. Data owner sends a permit ticket to the cloud
provider

In addition to sending the access ticket to the user, the
data owner prepares a message m 4 = {U-ID // D-ID // N u //
N d // ks // OP} and sends a permit ticket {O-ID, {m 4 // h (m 4

// SC) dc }} to the cloud provider. Upon receiving the permit
ticket, the cloud provider executes the following steps:

a) Decrypt the received message, using the symmetric
secret key, dc, (that is relevant to O-ID) and obtain
m 4 = {U-ID, D-ID // N u // N d // ks //OP}, and h (m 4
// SC).

b) Verify the format of D-ID from the decrypted
message m 4 . If there is no match, the cloud provider
terminates the connection. Otherwise, the cloud
provider continues.

c) Compute h (m 4 // SC) and checks whether it equals
the received h (m 4 // SC)). If there is a match, the
cloud provider ensures the authenticity of the data
owner.

d)Keep D-ID, U-ID, N w N d and ks for processing the
access certificate in step 4.

4. The user processes the access ticket and presents
access certificate to the cloud provider

Upon receiving the access ticket, {O-ID, {m 2 // {m 3 } dc //
h (m 2 // {m 3 } dc // SU)} dl J, the user executes the following
steps:

a) Decrypt the received message, using the symmetric
secret key, du, and obtain obtain m 2 = {C-ID //D-ID
// N u // // N d // EN, kd // h (data) // ks // OP}, {m 3 } dc ,
and h (m 2 // {m 3 } dc // SU)

b) Compare the values of D-ID and A^,,, obtained from
m 2 to those values sent in message m L If there is a
match, the user continues.

c) Compute h (m 2 // {m 3 } dc // SU) and checks whether
it equals the received h (m 2 // {m 3 } dc // SU). If there
is a match, the user ensures the authenticity of the
data owner.

d)Keep C-ID, ks, and N d for processing cloud provide
message, m 5 , in step 5.

e) Extract the encrypted access certificate, {m 3 } dc , from
the received access ticket, prepare a message m 5 =
{O-ID, {m 3 } dc }and present the access certificate, {U-
ID, m 5 // h (m 5 // ks)}, to the cloud provider to obtain
the specific data, defined by D-ID in message mi
and m 3

5. Cloud provider sends the encrypted data to the
user

Upon receiving the message, {U-ID, {m 5 // h (m 5 // ks)}
from a user, the cloud provider retrieves the one-session
key, ks, received from the data owner in message m 4 and
executes the following steps:

a) Compute h (m 5 // ks) and compares it with the
received h (m 5 // ks). If there is a match, the user
continues.

b) Extract {m 3 } dc from m 5 and then decrypt {m 3 } dc
using the symmetric secret key, dc, that is relevant

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

to data owner, O-ID and obtain m 3 = {U-ID // DID
//N u //N d //OP}

c) Compare the values of U-ID, D-ID, N u , N d , OP,
received from user in m 3 to those values obtained
from message m 4 received from the data owner. If
there is a match, the cloud provider authenticates the
user and continues.

d) Send the required data by preparing the message m 6
= {ENC {data)} XOR ks to the user.

e)Send a message = {C-ID, m 6 // h (m 6 // ks)} to the
user defined by U-ID.

Therefore, cloud providers would not be able to
grant data access to unauthorized users.
3. Authentication is achieved by using a hash code,
containing a secret anonymity (SU or SC) and
encryption by a secret encryption key (du or dc). For
example, as shown in Figure 4, the data owner
appends a secret user's anonymity, SU, to the
exchanged message, m 2 , before computing its hash
code, h (m 2 II SU). The data owner then encrypts the
exchanged message, [m 2 // h (m 2 // SU)} by the
secret symmetric key (du) and sends it to the user.

6. User verifies the received data from the cloud
provider

Upon receiving a message { C-ID, m 6 // h (m 6 // ks)}
from the cloud provider, the user retrieves the one session
key, ks, received from the data owner in m 2 , and executes
the following steps:

a) Compute h (m 6 // ks) and compare it with the
received h (m 6 // ks). If there is a match, the user
continues.

b) Compute m 6 XOR ks and obtain the encrypted data,
ENC {data}.

c) Decrypt the received encrypted data, ENC {data},
with the encoding key, kd, received from the data
owner in m 2 .

d) Compute h (data) and compare it with h (data)
obtained from the data owner in message m 2 . If
there is a match, the user ensures the integrity and
confidentiality of the received data.

III. SECURITY ANALYSIS OF THE ADVANCED
SECURITY-BASED MODEL

This section provides security analysis of the proposed
advanced security-based data sharing model. The analysis
demonstrates how the advanced security-based model
achieves the goals of securing data storage and sharing and
enhancing resiliency against cyber attacks in the cloud
environment.

A. Achieving data storage and sharing security goals

The advanced security-based model achieves the
security of data storage and sharing in the cloud
environments as follows:

1 . Since the data is stored in encrypted form on the
cloud and the data owner keeps the encryption
information (algorithm and key), the cloud storage
provider does not have the capability of
compromising the integrity and confidentiality of
the data stored in the cloud infrastructure.

2. The data owner is the only authority that
authenticates the user and issues the data encryption
information (algorithm and key) to authorized users.

Message \ha Secret

( m j/m, ) Anonymity

Hash Calculation Secret Key

I „ . ,, . — <aw

I Hash. V alue

Concatenate

Hash Calculatio

Concatenate

Secret Key
(du)

User Secret

Received
Hash Value

— I Decrypt I "\ Message and Hash Vahie

LserData Ov-tier Data Owner.User

Figure 4. Securing transmission between the data owner and the user

B. Enhancing Resilience Against Cyber-Attacks

The advanced security-based model offers resiliency
against various types of cyber-attacks as follows:

1) Resilience against Unauthorized data access attack

This advanced security-based model makes it extremely
difficult for unauthorized user to access data since an
unauthorized user must pass through the following advanced
security layers and authentication steps:

1 . Data owner authentication: The attacker must have
the knowledge of user anonymity, US, and the
encryption key, du, to request data access and pass
the authentication process controlled by the data
owner. The attacker would not be able to guess both
parameters needed to pass the authentication process
nor hack the data access ticket from the owner
required to be delivered to the cloud providers to
access data.

2. Cloud provider authentication: The attacker must
have the knowledge of the nonce, N u and N d , sent
from the data owner to the cloud provider in m 4 (m 4
= { U-ID IID-ID II N u // N d II ks II OP}), the cloud
provider encryption key, dc, and the one-time
session key, ks, to create an access certificate (U-ID,
m 5 II h (m 5 II ks)), (m 5 = { O-ID, {{U-ID // DID // N u
// N d IIOP}} dc }), and submit it to the cloud provider
in order to gain access to the encrypted data. The
attacker will not be able to guess the above
parameters needed to pass the authentication process
nor hack the data access certificate from the user
required to be delivered to the cloud providers to
access data. This additional authentication step in

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

the advanced security based model provides an
additional security layer to the cloud service
providers by allowing them to authenticate users'
identities before forwarding them the encrypted
data. The introduction of this additional security
layer ensures heightened security needed for highly
sensitive military and intelligence related
information in the cloud environment.
3. Data encryption: If somehow the attacker is able to
successfully pass the previous two steps, he/she
would not be able to decrypt the data received from
the cloud provide since the encryption information
(key and algorithm) is not known to unauthorized
user and the cloud provider.

2) Resilience against sharing attack

To acquire data during sharing, an attacker must have
the decryption key and algorithm. Under the proposed
advanced security based model, the decryption key and
algorithm remain in the data owner's domain. Therefore,
cloud storage providers and unauthorized users would not
be able to decrypt the data.

3) Resilience against user's identify guessing attack

This advanced security-based model uses hash code and
encryption concepts for the exchanged messages between
the data owner and a user. For example, as shown in Figure
4, an authorized user appends a secret user's anonymity to
the exchanged message (nij) before computing its hash
code, and then encrypts the exchanged message by the
secret symmetric key, du. Both secrets (SU, and du) are
known only to the authorized user. Since the hash code
provides authentication and the encryption provides
confidentiality to the exchanged message between data
owner and a user, an attacker may not guess the user's
anonymity from the exchanged messages and therefore
would not be able to imitate the user's identity to create a
new data access request.

4 ) Resilience against data owner's identify guessing
attack

Similarly, data owner uses hash code and encryption
concepts for the exchanged messages to authorized users.
Therefore, an adversary cannot guess the user's anonymity
from the exchanged messages and cannot imitate a data
owner and send fake data access to authorized users

5 ) Resilience against Cloud Provider's Identify
Guessing Attack

Since data owner uses hash code and encryption
concepts for the exchanged messages to the cloud provider,
an adversary cannot guess the cloud provider's anonymity
from the exchanged messages.

6) Resilience against Impersonation Attack

An impersonation attack involves an adversary that
attempts to impersonate a data owner, a user, or a cloud
provider. Under this advanced security based model, an
adversary may not be able to impersonate a data owner, a
user, or a cloud provider as follows: an adversary may not
be able to imitate a data owner to grant data access to a user
without knowing user secrets (SU, du), cloud provider
secrets (SC, dc), and data encryption information
(encryption algorithm, data encryption key). Without
knowing the user secrets (Su, du), an adversary would not
be able to imitate a user to decrypt the message m 2 to gain
unauthorized access to data. Since the cloud provider
doesn't know the data encryption algorithm, EN, the data
encryption key, M, and the message encryption key, ks,
(issued by the data owner to the authorized user), an
adversary would not be able to imitate a cloud provider to
allow access to unauthorized users.

7) Resilience against Replay Attack

A replay attack is a method in which an adversary
attempts to replay messages obtained during past
communications. An attacker may replay the used message
(mi) to the data owner requesting data access and then
receiving the message (m 2) from data owner. However,
under this advanced security based model, the attacker will
not be able to derive correct data information (data ID, data
encryption algorithm, and data encryption key) from m 2
since the attacker cannot decrypt m 2 without knowing the
user secrets(5'C/, du). In addition, the attacker will not be
able to decrypt message (m 5 ), received from the cloud
service provider, since the attacker cannot retrieve the one
time encryption key, ks, issued by data owner in message,
m 2 . An attacker may replay the used access message, (m 5 =
[O-ID, {m 3 j dc } = {O-ID, {{U-ID // DID // N u // N d
//OPJJdc}), to the cloud provider to gain access to the
encrypted data. Since the cloud provider will recognize that
message nonce (N u and Nd) has been expired, the cloud
provider will ignore the received access message and will
not provide data access to the attacker.

IV. CONCLUSION

This paper has introduced an advanced security-based
data sharing model that offers authentication and policy
confirmation capabilities to cloud service provider while
maintaining data owner full control capability, security
requirements achievement, and resiliency to several cyber
security threats in the cloud environment. The proposed
advanced model can be used to secure data sharing of highly
sensitive information stored by military and intelligence
organizations in the cloud environment. The proposed
advanced security-based model offers flexibility for each
application to use its own unique data format and encryption
technique for facilitating secure data sharing in the cloud
environment.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

References

[1] M. Luo, L. Zhang, and F. Lei, "An Insuanrance Model for
Guranteeing Service Assurance, Integrity and QoS," IEEE
International Conference on Web Services, pp. 584-591, 2010

[2] T. Sridhar, "Cloud computing - a primer, Part 1: models and
technologies," The Internet Protocol Journal, vol. 12 (3), pp. 2-19,
September 2009.

[3] Salesforce Inc., 2011. Retrieved from Salesforce Inc.:
http://www.salesforce.com/.

[4] Google Inc., "Google app engine," 2011, retrieved in March 2011
from http://appengine.google.com

[5] Amazon Inc., "Simple storage service," 2011, retrieved in March
2011 from http://aws.amazon.com/s3.

[6] K. Fogarty, "Cloud computing's top security risk: how one company
got burned" 20 1 0, retrieved from

http://www.cio.com/article/599473/Cloud_Computing_s_Top_Securit
y_Risk_How.

[7] K. Hamlen, M. Kantarcioglu, L. Khan, and B. Thuraisingham,
"Security issues for cloud computing," International Journal of
Information Security and Privacy , vol. 4 (2), pp. 39-51, 2010.

[8] G Zhao, C. Rong, J. Li, F. Zhang, and Y. Tang, "Trusted data sharing
over untrusted cloud storage providers," 2nd IEEE international
conference on cloud computing technology and science, pp- 97-103,
2010

[9] W. Wan and Z. Li, "Secure and efficient access to outsourced data,"
16th ACM conference on computer and communication security,
2009.

[10] M. Meky and A. Ali, "A Novel and Secure Data Sharing Model with
Full Owner Control in the Cloud Environment," International Journal
of Computer Science and Information Security, vol. 9 (6), pp. 12-17,
2011.

[11] W. Diffie and M. Hellman, "New directions in cryptography," IEEE
Transactions on Information Theory , vol. 22 (6), pp- 644-654, 1976.

AUTHORS PROFILE
Mohamed Meky is an Adjunct Professor of Cybersecurity at the Center for
Security Studies, University of Maryland University College (UMUC). He
has more than fifteen years of experience in industrial, research, and
teaching. He has published several articles in IT field and cybersecurity.
His current research interest is in the cybersecurity.

Amjad Ali is the Director of the Center for Security Studies and a Professor
of Cybersecurity at University of Maryland University College. He played
a significant role in the design and launch of UMUC's cybersecurity
programs. He teaches graduate level courses in the area of cybersecurity.
He has served as a panelist and a presenter in major conferences and
seminars on the topics of cybersecurity and innovation management. In
addition, he has published several papers in the area of cybersecurity.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

Secure and context-aware routing in mobile ad-hoc

networks

r.haboub and m.ouzzif

RITM laboratory, Computer science and Networks team

ESTC - ENSEM - UH2 Casablanca, Morocco

rachidhaboub@hotmail.com and ouzzif@gmail.com

Abstract - The increasing availability of wireless handheld devices
and recent advances in Mobile Ad-hoc networks (MANET) open new
scenarios in which users can benefit from anywhere and at anytime
for impromptu collaboration. However, nodes energy constraints, low
channel bandwidth, node mobility, channel variability and packet loss
are some of the limitations of MANETs. Instead of handling packet
loss, in this work, we propose an approach to reduce packet loss by
avoiding the conditions in which packet losses are likely, using a
context-aware routing approach, which selects the optimal path from
source node to the destination node. The proposed approach was
tested and the results show an interesting reduction of packet loss.

Keywords - MANETs, context aware routing, packet loss.

Introduction

The demand of smart phones, laptops and PDAs has grown
exponentially each year since their introduction. These mobile
devices can be used to form a MANET. A MANET consists of
arbitrary deployed communicational devices such as cell
phones, personal digital assistants (PDAs), laptops, etc; it is a
wireless multi-hop network, where all nodes maintain network
connectivity cooperatively. The mobile nodes are capable of
connecting and communicating with each other using limited
bandwidth radio links. These types of networks are useful in
any situation where temporary network connectivity is required
and in areas where there is no infrastructure, such as disaster
relief, where existing infrastructure is damaged, or in military
applications where a tactical network is necessary.

Figure 1 . MANETS can change the topology

In the battlefield, typically in a foreign land, one may not
rely on the existing infrastructure. In these situations,

establishing infrastructure is not practical in terms of
expenditure and time consumed. Hence, providing the needed
connectivity and network services becomes a real challenge. In
a wireless ad hoc network where pairs of mobiles communicate
by exchanging a variable number of data packets along routes
set up by a routing algorithm, reliability may be defined as the
ability to provide high delivery rate, that is, to deliver most of
the data packets in spite of faults breaking the routes or buffer
overflows caused by overloaded nodes. Given the intrinsic
nature of wireless ad hoc networks, reliability is a major issue.

Links failures may occur due to interferences on the
wireless medium, or, most probably, to nodes mobility, when
pairs of nodes move out of the reciprocal transmission range or
are shadowed by obstacles. MANETs do not only provide
dynamic infrastructure networks but also allow the flexibility
of wireless devices mobility. MANETs differ significantly
from existing networks. First, the topology of the nodes in the
network is dynamic. Second, these networks are self-
configuring in nature and do not require any centralized control
or administration. Such networks do not assume all the nodes
to be in direct transmission range of each other. Hence these
networks require specialized routing protocols that provide
self-starting behavior.

However energy constrained nodes, low channel
bandwidth, node mobility, high channel error rates, and
channel variability are some of the limitations of MANETs.
Under these conditions, existing wired network protocols
would fail or perform poorly. Thus, MANETs require
specialized routing protocols. Operating power is one of the
most important resources required by wireless devices [20]. For
practical use, wireless devices can only store electricity in
relatively small quantities. This makes it necessary to consider
conservation measures that reduce consumption of electricity
by the equipment. In a related issue, current technology
employed by batteries is not sufficient to power wireless
devices for long periods.

Thus, energy conservation is one of the few strategies that
can really make a difference in the context of mobile device
usage. Given the scarcity of power as an operational resource
on mobile systems, it is important to notice that there is still no
satisfactory solution that provides long-term service and/or low

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

power consumption. In this work we propose an approach
which tries to find the optimal path from source node to
destination. This paper is organized as follow: the next section
shows MANETs applications, section three discusses some
routing protocols, section four describes the problematic,
section five gives some related works, section six present the
proposed approach, the simulation is presented in section seven
and the last section conclude this work and gives some
perspectives.

II. MANETs APPLICATIONS

MANETs are specifically designed for particular
applications [14]. This section discusses potential applications
which motivate deploying this kind of networks. MANETs can
be used in collaborative networks. A typical application of a
collaborative MANET can be considered as a conference room
with participant's wishing to communicate with each other
without the mediation of global Internet connectivity. In such
scenario, a collaborative network can be set up among the
participants' devices. Each participant can thus communicate
with any other participant in the network without requiring any
centralized routing infrastructure. These networks are thus
collaborative in nature and are useful in cases where business
network infrastructure is often missing or in scenarios where
reduction in the cost of using infrastructure links is important.

Figure 2. MANETS applications

MANETs can be used in distributed control systems.
MANETs allow distributed control with remote plants, sensors
and actuators linked together through wireless communication.
These networks help in coordinating unmanned mobile units
and lead to a reduction in maintenance and reconfiguration
costs. MANETs are used to co-ordinate the control of multiple
vehicles in an automated highway system, coordination of
unmanned airborne vehicles, and exploration of new
geographical areas, rescue, medical applications, mobile

systems in a conference and remote control of manufacturing
units.

III. Routing protocols

A protocol is a set of rules that must be followed by
partners during a communication process. Without a protocol,
messages sent on a network have no meaning, therefore,
connection between nodes cannot be established and as result,
no information is transferred. Protocols are a required part of
the logical structure of a computer network.

There are two main categories of routing protocol (Fig. 3):
proactive [10] and reactive [15]. Proactive protocols maintain
fresh lists of destinations and their routes by periodically
distributing routing tables throughout the network. Reactive
protocols find a route on demand (only when needed) by
flooding the network with Route Request packets. Studies
show, that proactive protocols perform poorly when the
mobility increase because of excessive routing overhead [1,2].

ROUTING PROTOCOLS

PROACTIVE

REACTIVE

DESTINATION

SEQUENCED DISTANCE

VECTOR

AD HOC: ON-DEMAND
DISTANCE VECTOR

OPTIMIZED LINK. STATE
ROUTING

DYNAMIC SOURCE
ROUTING

Fish eye State Routing

TOPOLOGY BROADCAST
BASED ON REVERSE
PATH FORWARDING

Figure 3. Routing protocols classification

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

IV. PROBLEM STATEMENT

Despite of long history of MANETs, there are still a quite
number of problems that are open for the research community.
Routing is one of the most important open issues in MANETs
research. When nodes want to communicate with each other,
they must initially discover a suitable route to be used for the
communication. The high mobility, low bandwidth, and limited
computing capability characteristics of mobile hosts make the
design of routing protocols very challenging. The protocols
must be able to keep up with the rapid unpredictable changes in
network topology with as minimal control message exchanges
as possible and in the most efficient and reliable way. An
essential problem concerning ad hoc wireless networks is to
design routing protocols allowing for communication between
mobile hosts. The dynamic nature of ad hoc networks makes
this problem especially challenging.

In any network communication there is a need for a suitable
routing mechanism to deliver packets from source to
destination nodes. The concept of routing can be described as
the process of path finding. In case of mobile ad hoc networks
(MANETs) the main problem in routing mechanism, is how to
send a packet from one node to another when no direct link
exist between source and destination nodes. The routing
protocol must also be aware of MANETs limitations. Using for
example node's energy in a non-efficient way, may lead to
some nodes death, which lead to breaking links and packets
loss.

V. Related works

Different approaches have been proposed in the research
community to improve the reliability of packet-delivery in
different application scenarios. Forward Error Correction
(FEC) approaches [3,4] and multipath transmission approaches
[8] are proposed to improve the reliability of packet-
transmission by adding redundant information into transmitted
data. With the redundant information, some bit errors can be
eliminated without retransmission. Data-fusion mechanisms
[16] can change the structure of data packets to distribute
transmission errors across multiple packets.

Geographical routing protocols [7] use nodes location
information to determine link status and avoid links between
distant nodes. On-demand routing protocols [5] check the
status of links by exchanging a small control message before
the transmission of any data packet. These approaches reduce
the probability of transmitting a data packet through a link that
has already failed. Most proposed on-demand routing protocols
(for example, Dynamic Source Routing (DSR) [6] and Ad hoc
On-demand Distance Vector (AODV) [5, 12]) however, use
single route for each session.

SPAN [18] selects a number of coordinators to keep an ad
hoc network connected. These coordinators are responsible for
forwarding and buffering packets while others sleep. Thus it
saves power without significantly diminishing the capacity or
connectivity of the network. However it introduces large delays
and is not applicable for time critical applications. Some

routing algorithms given by [20, 21] can optimize the energy
use with a global perspective. But these algorithms cause
expensive overheads for gathering, exchanging and storing the
state information of a node. Power control techniques have
direct impact on routing strategies for MANETS.

Therefore, much of the work on power control has been
concentrated on the development of new protocols that can
minimize the used power. For example, Jung and Vaidya [21]
provide a new protocol for power control, based on information
available through lower level network layers. Another example
of this approach is given in Narayanaswamy et al. [22]. A
simple strategy for minimizing the power consumption in a
network consists of trying to reach the state of minimum power
in which the network is still connected. Despite the apparent
optimality of this technique, Park and Sivakumar [23] have
shown that this is not necessarily an optimum power strategy
for power control minimization. Still another approach for
power control is presented by Kawadia and Kumar [24]. Two
protocols are proposed, in which the main technique used is
clustering of mobile units according to some of its features.

VI. Approach

If the motion parameters of two neighboring nodes like
speed, direction, radio propagation range are known (using for
example a Global Positioning System (GPS)), the duration of
time these two nodes will remain connected can be determined.
Assume two nodes i and j within the transmission range of each
other. Let (xi, yi) be the coordinates of node i and (xj, yj) be the
coordinates of node j. Let Vi and Vj be the speeds, ei (0< ei )
and ej (ej < 211) be the directions of motion for nodes i and j,
respectively. The amount of time two mobile hosts will stay
connected, is predicted by the formula given by equation:

Link time life

-(ab + cd) + V0 2 + c 2 )r 2 -(ad-bc) 2
a 2 + c 2

a = V(r)Cose-V(s)Cos9

b = X(r) - X(s)

c = V(r)Sin6 - V(s)SinB = V(Y(r)) - V(Y(s))

d = Y(r) - Y(s)

r is the transmission range of a wireless node with an Omni-
directional antenna, which is 250 m. V(s) and V(r) are the
velocities of the sender and receiver respectively. is the
direction of motion of nodes. (Xs,Ys) and (Xr,Yr) are the
coordinates of the sender and receiver respectively. Parameter
"a" is the relative velocity of the receiver node with respect to
the sender node along Y axis, "b" is the parameter used to
determine the distance of the receiver node from the sender
node along X axis. The third parameter used to determine LET
is "c", which is the relative velocity of receiver node with

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

respect to the sender node along Y axis, "d" is the distance of
the receiver node from the sender node along Y axis.

Consider a MANET consisting of four nodes illustrated in
Fig. 4, figures a, b and c represent the network topologies for a
non mobility aware protocol at times t, t+1 and t+2,
respectively. Figures d, e and f narrate the expected network
topologies for the MARP (Mobility Aware Routing Protocol)
approach at times t, t+1 and t+2, respectively. Node and Node
3 are assumed to be sender and receiver nodes respectively. A
non-mobility aware protocol would use route 0-1-2. If node 1 is
moving away out of the transmission range of node 3, the link
1-3 breaks. This event initiates route maintenance activity
which results in heavy control traffic generated by node and
node 3 in an attempt to revive the broken link but in vain.

It forms route 0-2-3 to retain the network data transmission.
Apart from high control traffic generated, the active
transmission of data through these links during a link
disconnection results in loss of data packets. Both the excess
control overhead generated to revive the broken links and the
data packet loss could have been avoided if a more reliable
route 5-4-3-1 was formed instead of 5-4-2-1. This can be
achieved with the implementation of the MARP routing
algorithm in the underlying routing protocol. With MARP, the
fast moving node, node 2, is eliminated from route discovery
process by node 1 and the routing protocol forms the route
through node 3 instead.

Figure 4. Mobility awareness

When a node wants to send data, it starts sending "RREQ"
packets to find a path to the destination node. Our contribution
is to delete the "RREQ" messages when it reaches a node with
low energy, in this way, this node will not play the role of a
router (which means that this node will not consume additional
energy by acting as a router), but it can continue to play its
oversight role, in the case of a military network of wireless
sensors for example.

Existing works

Approach

%
1

g
S.

•-•
i i

Figure 5. Energy awareness

//Getting the receiver coordinates.
Xr, Yr, Zr

//Getting the receiver velocities.
V(Xr), V(Yr), V(Zr)

//Getting the sender coordinates.

Xs, Ys, Zs

//Getting the sender velocities.
V(Xs), V(Ys), V(Zs)

//Calculating the link expiration time,
double a = V(Xr)-V(Xs);
double b = Xr-Xs;
double c = V(Yr)-V(Ys);
double d = Yr-Ys;

// The average transmission range of a wireless node with an 20
Omni-directional antenna is 250 m.
double r = 250;

double P = (((a*a)+(c*c))*(r*r))-(((a*d)-(b*c))*((a*d)-(b*c)));
float Q;

if(P>=0) {Q = sqrt(P); }

else { Q = sqrt(-(P)); }

if (((a*a)+(c*c)) == 0.0) {// LET will have an infinite value. }
else { LET = (-l*((a*b)+(c*d))+Q)/((a*a)+(c*c));}

//If LET or the node's energy is too low, drop the RReq packets.

Figure 6. Context awareness algorithm

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

VII. Simulation

We use the NS-2 simulator [9]. Figure 7 gives a simplified
View of NS-2. NS-2 takes as an input a TCL file (in which we
implement the scenario). NS2 consists of two key languages:
C++ and Object-oriented Tool Command Language (OTcl).
While the C++ defines the internal mechanism of the
simulation objects, the OTcl sets up simulation by assembling
and configuring the objects as well as scheduling discrete
events.

Analysis

OTcl: Tel mreipreter with OO
extent 10 n

OTcl script
Simulation
Program

NS Sinmlaor Library
(event scheduler objects,
network component objects
and network setup helping
modules (Plumbing Modules)

-*•

Simulation
results

NAM

Network

Animator

Figure 7. Simplified view of NS2

NS2 is an object oriented, discrete event driven network
simulator. It is written in C++ and OTcl (Object-oriented Tool
command language) script language with Object-oriented
extensions developed at MIT (Massachusetts Institute of
Technology). In order to reduce the processing time, the basic
network component objects are written using C++. Each object
has a matching OTcl object through an OTcl linkage. The
procedure of using NS2 [24] to simulate the network and
analyze the simulation result is as follows. Firstly, the user has
to program with OTcl script language to initiate an event
scheduler, set up the network topology using the network
objects and tell traffic sources when to start and stop
transmitting packets through the event scheduler. OTcl script is
executed by NS2.

The simulation results from running this script in NS2
include one or more text based output files and an input to a
graphical simulation display tool called Network Animator
(NAM). Text based files record the activities taking place in
the network. It can be analyzed by other tools such as Gwak
and Guplot to calculate and draw the results such as delay and
jitter in form of figures. NAM is an animation tool for viewing
network simulation traces and real world packet traces. It has a
graphical interface which can present information such as
number of packets drops at each link. After simulation, NS2
outputs a trace file, which can be inteipreted by many tools,
such as NAM and Xgraph. We create a simulation scenario
using NS-2 Scenario Generator [11].

Fig. 8 shows the NS-2 trace file format. The first field is
event, it gives many possible symbols ( 'r', 'd', etc. ). These
symbols may correspond for example to received and dropped
packets. The second field gives the time at which the event
occurs. The third field gives the source node at which the event
occurs. The fourth field gives the destination node at which the

event occurs. The fifth field shows information about the
packet type, whether it's a UDP or a TCP packet. The sixth
field gives the packet size. The seventh field gives information
about some flags. The Fid field is the flow Id, it can be used for
specifying the color of flow in NAM display. The ninth field is
the source address. The tenth field is the destination address.
The eleventh field is the network layer protocol's packet
sequence number, and the last field shows the unique id of a
packet.

0)
Q.

to
Q

Figure 8. NS-2 Trace File format

We use the Java-Trace-Analyzer to interpret the trace file
generated by the simulation. Our approach was simulated using
our secure protocol [26]. Figure 9 shows that the proposed
approach reduce packet loss.

Li Approach

° 20 -

50 100 150 200 250 300 3_
Time (s)

Figure 9. Packet loss versus time

VIII. Conclusion and future works

In this work we propose an approach to reduce packet loss
in MANETs by avoiding conditions in which packet losses are
likely, by using a context-aware routing technique, which
selects the suitable routing path from source node to
destination, according to nodes states. For future work we plan
to use more experimentation metrics (such as nodes power,
mobility, transmission delay, network vicinity, etc.). We also
plan to consider more criteria like connectivity, and so on.

References

[1] J. Broch, D.A. Maltz, D.B. Johnson, Y.-C. Hu, and J. Jetcheva, .A
"Performance Comparison of Multi-Hop Wireless Ad Hoc Network
Routing Protocols". Proceedings of ACM/IEEE MOBICOM'98, Dallas,
TX, Oct. 1998, pp. 85-97.

[2] P. Johansson, T. Larsson, N. Hedman, B. Mielczarek, and M.
Degermark, "Scenario-based Performance Analysis of Routing Protocols
for Mobile Ad-hoc Networks". Proceedings of ACM/IEEE
MOBICOM'99, Seattle, WA, Aug. 1999, pp. 195-206.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

[3] J. Gemmell, L. Rizzo, M. Handley, J. Crowcroft, M. Luby and L.
Vicisano, "Forward error correction (FEC) building block". Internet
experimental RFC 3452.

[4] L. Vicisano and M. Luby. "Compact forward error correction (FEC)

schemes", Internet experimental RFC 3695.
[5] Georgy Sklyarenko, "AODV Routing Protocol", Takustr. 9, D-14195

Berlin, Germany.

[6] Rakesh Poonia, Amit Kumar Sanghi and Dr. Dharm Singh, "DSR
Routing Protocol in Wireless Ad-hoc Networks: Drop Analysis",
International Journal of Computer Applications (0975 - 8887), Volume
14- No.7, February 2011.

[7] Jorge Ariza , "Geographic Routing Protocol vs. Table Driven Protocols",
Seminar Routing Algorithmen.

[8] Stephen Mueller, Rose P. Tsang, and Dipak Ghosal, "Multipara Routing
in Mobile Ad Hoc Networks: Issues and Challenges".

[9] Kevin Fall, Kannan Varadhan, "The NS manual", May 9, 2010.

[10] Guoyou H., "Destination-Sequenced Distance Vector (DSDV)
protocol". Technical report, Helsinki University of Technology, Finland.

[11] Jia Huang, Hamid Shahnasser, "A preprocessor Tel script generator for
NS-2 communication network simulation", San Francisco State
University, USA, pp. 184-187, 5 May 2011.

[12] M.Devi and Dr.V.Rhymend Uthariaraj, "Routing with AODV Protocol
for Mobile ADHOC Network", International Journal of Technology And
Engineering System, Jan - March 201 1- Vol2. Nol.

[13] Huda Al Amri, Mehran Abolhasan, Tadeusz Wysocki. "Scalability of
MANET Routing Protocols for Heterogeneous and Homogenous
Networks". International Conference on Signal Processing and
Communication Systems. Dec 2007.

[14] I. Chlamtac, M. Conti and J. J.-N. Liu. "Mobile ad hoc networking:
imperatives and challenges". Ad Hoc Networks, Vol.(l), pages 13-64,
2003.

[15] Changling Liu and Jorg Kaiser, "A Survey of Mobile Ad Hoc network
Routing Protocols", 2008.

[16] F. Ye, G. Zhong, S. Lu and L. Zhang, "A robust data delivery protocol
for large scale sensor networks", in Proceedings of IPSN'03, pages
658.673, Palo Alto, CA, USA, April 22-23, 2003.

[17] D.E. Comer, "Internetworking with TCP/IP", Vol. 1: "Principles,
Protocols, and Architecture" (PrenticeHall, Englewood Cliffs, NJ, 1991).

[18] B. Chen, K. Jamieson, H. Balakrishnan, R. Morris, "SPAN.an energy
efficient coordination algorithm for topology maintenance in ad hoc
wireless networks", Wireless Networks, Kluwer Academic Publishers
2002.

[19] Chang J. H. and Tassiulas L. "Energy Conserving Routing in Wireless

Ad hoc Networks", Proceedings of IEEE INFOCOM'00, pp. 22-31,

2000.
[20] M. Min and A. Chinchuluun. Optimization in wireless networks. In

Handbook of Optimization in Telecommunications, pages 891-915.

Kluwer, Dordrecht, 2006.

[21] E. Jung and N. H. Vaidya. A power control mac protocol for ad hoc
networks. In ACM MOBICOM 2002, Atlanta, USA, 2002. ACM.

[22] S. Narayanaswamy, V. Kawadia, R. Sreenivas, and P. Kumar. Power
control in ad-hoc networks: Theory, architecture, algorithm and
implementation of the compow protocol. In European Wireless
Conference - Next Generation Wireless Networks: Technologies,
Protocols, Services and Applications, pages 156-162, Florence, Italy,
2002.EW2005.

[23] S.-J. Park and R. Sivakumar. Load sensitive transmission power control
in wireless ad-hoc networks, hi IEEE Global Communications
Conference (GLOBECOM'02), Taipei, Taiwan, 2002. IEEE.

[24] V. Kawadia and P. Kumar. Power control and clustering in ad hoc
networks. In IEEE INFOCOM'03, San Francisco, CA, 2003. IEEE.

[25] Mathematical Aspects of Network Routing Optimization, Springer,
USA, August 30, 2011.

[26] Rachid Haboub and Mohammed Ouzzif, SECURE AND RELIABLE
ROUTING IN MOBILE AD-HOC NETWORKS, International Journal
of Computer Science & Engineering Survey (IJCSES) Vol.3, No.l,
February 2012.

AUTHORS

Rachid Haboub is a full time Ph.D student. He
received the Master degree in computer science
in 2009. His research spans wireless
communication.

Dr. Mohammed Ouzzif is a professor in the
computer science department of the higher
school of technology of Casablanca - Hassan II
university of Morocco.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

Non-Linear Attitude Simulator of LEO Spacecraft
and Large Angle Attitude Maneuvers

31051243

Azza El-S. Ibrahim

Electronics Research Institute:

Computers & Systems dept.

Giza, Egypt

e-mail: azza@eri.sci. eg

Ahamed M. Tobal

Electronics Research Institute:

Computers & Systems dept.

Giza, Egypt

e-mail: atobal@eri.sci. eg

Mohammad A. Sultan

Cairo University, Faculty of

Engineering: Electronics &

Communications dept.

Giza, Egypt

e-mail: msultan@cu.edu. eg

Abstract — the Attitude control problem of a rigid spacecraft with
highly nonlinear characteristics has attracted a great interest for
its important applications. The main challenge of designing the
non-linear attitude control is the controller parameters
determination. Most of the early published works use trial and
error method for these parameters computation, but it is not
accurate and takes a lot of time to get the appropriate
parameters. Therefore, this paper presents a novel mathematical
way for sliding mode control parameters calculation at every
initial attitude state, taking into consideration the actuator
saturation constraints. The developed attitude control ensures
shortest angular path maneuvers. The objectives are
accomplished by building the microsatellite simulator using
MATLAB/Simulink software. In addition, the chattering
problem of the SMC technique is solved using the saturation
function. A system stability based on Lyapunov's direct method
is presented. Numerical simulations are performed to show that
rotational maneuver is accomplished in spite of the presence of
disturbance torques, and control saturation nonlinearity. The
results are compared with the conventional PD control technique.

Keywords-MATLAB/Simulink; sliding mode control; satellite
attitude control

Introduction

Attitude determination and control system (ADCS) is one
of the most crucial subsystems of the spacecraft. Main function
of ADCS is to stabilize the spacecraft, and steer it to a
particular direction correctly despite the internal and external
disturbance torques acting over spacecraft. Satellite hardware
structure and Simplified block diagram of ADCS is shown in
Fig. 1.

Attitude control is an important task for the satellite optical
payload and for remote sensing applications which, guarantees
pointing towards the ground area of the desired image. In
general, the spacecraft motion is governed by the so-called
kinematic and dynamic equations. Actually, mathematical
descriptions are highly nonlinear and thus, the conventional
linear control techniques are not suitable for the controller
design, especially when large-angle spacecraft maneuvers are
required [11]. There are several methods trying to solve this
problem, where some research linearized the nonlinear attitude
equations then applied different linear controls [7]. Doruk
utilized integrator back - stepping method which, provides a

Figure 1. a) Satellite hardware structure, b) Attitude control system block
diagram

recursive stabilization methodology [5]. Nadir-pointing control
is achieved by a full-state feedback Linear Quadratic Regulator
which drives the attitude quaternion and their respective rates
of change into the desired reference [14].

Sliding Mode Control (SMC) which is a particular type of
control, known as variable structure control (VSC), is a
powerful and robust control technique, and it has been
extensively studied in the last three decades for many classes of
linear and non-linear systems [1, 2, 3, 11]. For example, SMC
has been applied for mobile robot and welding processes [16],
it combined with fuzzy to control robot manipulators [17], also
used for linear time varying systems [18]. Recently, some
investigators [4, 8, 9 and 13] have applied the sliding-mode to
the spacecraft attitude maneuvers.

This paper presents a control system design method for
the three-axis-rotational maneuver of a rigid spacecraft. The
design of attitude controller was based on variable structure
control (VSC) theory leading to a discontinuous control law.
Most of the works done in this area did not target how the
controller parameters can be chosen. Other works advise to

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

compute them by trial and error method. A novel
mathematical way is introduced here to deal with the
controller parameters adjustment of attitude tracking problems
depending on the actuator saturation limits and on the reaching
time. Furthermore, the chattering in control signal is avoided
using a boundary layer approach. The thickness of the layers is
not constant but they are related to the feedback gains such
that the slope of the linear control inside the layer is constants.
The paper is organized as following: In Section II satellite
kinematics and dynamics are defined and modeled in state
space form. The explanation of the different models used in
simulation is mentioned in section III. Section IV discuses the
design of SMC control which acts as the attitude controller for
the satellite. Control parameters selection methodology is
described in section V. Section VI shows the stability analysis
of the controller. Section VII shows the way of dealing with the
chattering problem. Numerical simulations and comparative
study have been performed and presented in the last section.

II. Satallite Mathematical Model

The equations of motion of satellite attitude dynamics can
be divided into two sets: kinematic equations of motion and
dynamic equations of motion.

A. Dynamic Equation of Motion

The dynamic equations of a rigid spacecraft actuated by
reaction wheels can be defined in (1) [5, 6].

(1)

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
the total disturbance torques action on the satellite from variety
of sources, T a is the control torque generated by the reaction
wheels.

!<>>b = T dist -o) l b x (lco l b + h w ) - h M

In (1), the angular acceleration of the satellite referenced to
the inertial coordinate system is influenced by the disturbance
torque T dist , and the control torques exerted by a combinations
of reaction wheels h w . The satellite moment of inertia, / was
estimated by the mass and the shape of the body. The
derivation steps of the satellite model starts with the definition
of the relative angular momentum of the reaction wheel rotors,
h w = I s a) s , I s is the mass moment of inertia of the reaction
wheels and a) s is the angular velocity of the reaction wheel
rotors with respect to the body reference frame [6]. For nadir-
pointing satellite, the inertial referenced satellite body angular
velocities are converted to the orbit frame by the fact that
a)g b — a)f b — RgO)f therefore the dynamic differential
equations become as in (2) and (3).

6>ob = r^S^ob - (*>oC 2 ){l(<*>ob ~ o c 2 ) + AI s a> s )] +

/-^-/-M^-SO^H^ (2)

d> s = -A T ]- 1 [-S(a> b ob - u c 2 )(l(a) b ob - a> c 2 ) + AI s a> s )] -

ATj-^ + iATj^A + I- 1 ]^ (3)

Where, a>° is the orbital angular velocity vector and
assumed to be constant in circular orbits and equal to co° =

[0 — a> 0] T , a) — |-| where Li e is the gravitational

parameters, a is the orbit semi-major axis, R„ = [ c i c 2 c 3]
is the rotation matrix from orbit frame to body frame and J = [I
- AI S A T ] is the inertia-like matrix and A is the reaction wheel
orientation matrix. The three reaction wheels are mounted
along the body principle axes so A is the identity matrix. T e is

B. Kienamatic Equatios of Motion

The attitude is assumed to be represented by the quaternion,
defined as q = [q v q 4 ] T . The kinematic equation through unit
quaternion representation is given in (4):

<?•= i»(q)w* b

Where, S (q) =

matrix and

^4^3X3 +

[q v x ]

y -qi

lq„xl =

--<?2

(4)

7 3X 3 is a 3x3 identity

-<7s

<7i

<?2

is a skew

symmetric matrix

C. System Errors

The error between the desired attitude and current attitude
is calculated in quaternion form. Let q e= [q V e <?4e ] T denotes
the relative attitude error from a desired reference frame to the
body-fixed reference frame of the spacecraft, then one may
have: q e = q ® q^ 1 where, q^ 1 is the inverse of the desired
quaternion, q is the spacecraft current quaternion and ® is the
operator for quaternion multiplication. For any given two
groups of quaternion, the relative attitude error can be defined
by (5) obtained by:

Hve
({4e

Q4e I + kv

ft) g (t)

(5)

Where,w e = a) - a) d , a) d is the desired angular velocity of
the body and assumed to be zero so a) e = a) and q ve is the
attitude quaternion error direction vector.

III. Simulation Modules

This section describes the simulation process. The
simulator is built using simulink toolbox of Matlab version
7.5.0.342 (R2007b).

The overall system simulator includes many subsystems or
modules. The main modules with their inputs and outputs are
shown in Fig. 2

Block (1). "System constant parameters": all satellite
physical parameters are gathered in a simulink block so any
satellite configuration can be easily used.

Block (2). "Euler to q": the attitude is represented in
quaternion form to avoid singularity and complex computation
in trigonometric functions in rotation matrix between satellite
body frame and orbit frame. Because of the quaternion
representation is not physically clear so building a
transformation block to transfer from "Euler angles to
quaternion" and from "quaternion to Euler" is necessary [10].

Block (3). "qe-calculation": quaternion error calculation

module is built using (5).

Block (4). "SMC Controller": the sliding mode control is
designed and constructed later in the next section.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

trrr?rS\ Intemntinnnl In,,™,,! r,f r„ m n„tv r Vr-

nrl Infnrrnntinv, V-'CllHty,

), 2012

(1)

# desired Attitude
in Euler angles

-►

E_d

initE

# system constant parameters

r>E(ln

n deg) q (q1,q2,q3,q4)

qrj qe13-

-►qe13

(2)
# Euler to q

-► qe4

[0;0;0]

wbob(O)

[.0001 ;.0001 ;.0001 ]

#disturbance taw e

tawa

q_d
c2

(3)

quaternion -error

calculation

we(t)

Manual Switch I

► u E(u)-

,s -

" K. 1 )

# Body_ang .velocity
wbo_b (wx wy wz)

latrix
Itiplj

Hw
— ►

# Wheel _ang .velocity
Ws (wsx wsy wsz)

qbob - L _

# desired
Ang. velocity

contra -energy plant +RWs_actuator

E_(u) Dynamics (N.L)1

(4) (5)

# SMC controller # plant system

u quat2R R — ► input_matrixA(E) alphs

a/c\ Subsystem

K - calculation

-►< kold

(6)

controller parameters selection

-►qe13

— ► qe4 qe13-dot_ini -

— ► qe dotini

— ► wbobe

c_calculation subsystem

-►qe(O)

Figure 2. Overall system simulator

-*F

-> 4

# wbi-b

-> 3

ws rpm

to rpm — r

(10)

# reaction wheel

momentum

and angular _velocity

# Attitude
qbo_b (q1 q2 q3 q4)

><M

alpha (deg }

alpha error

# quat .Display

# output

Euler-angels

Display

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

Block (5). "Plant System": the kinematic and dynamic
equations are constructed in vector form. These coupled
equations must be solved simultaneously

Numerical integration method is used to obtain the time
evolution of body attitude and angular velocity referred to the
orbit frame. For ensuring that the construction of this module is
done correctly, a special case for getting analytic solutions is
used in Wertz [6]. The idea assumes that the external torques
equal to zero and for axial symmetric body case, equation (1)
can be solved then the three component of the body angular
velocity can be obtained analytically and compared with the
output of the simulator. The kinematic equations become first
order differential equations which, can be solved separately and
the output is checked.

IV . Sliding Mode Attitude Control Design

Sliding mode control has been widely applied [13, 17, 18]
The conventional SMC design approach consists of two steps.
First, a sliding manifold is designed such that the system
trajectory along the manifold acquires certain desired
properties, then a discontinuous control is designed such that
the system trajectories reach the manifold in finite time.
Therefore the objective is to develop an attitude control law
such that q -> q d and a> -> as t -> oo under the assumptions
of bounded external disturbance, \T dist (t)\ < d max and the
unit-quaternion q and the angular velocity a) are available in
the feedback control design.

A. Sliding Surface Design

A linear sliding surface in vector form is defined in (6) [1].

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
model uncertainties in the system. After differentiating (6)
with respect to time equate it by zero.

s — to + c sgn(q e4 ) q e

(6)

Where s(t) = ^ s 2 s 3 ] T £ E 3X1 and c is a strictly positive
real constant vector determining the slope of the sliding
surfaces. Vadali and Crassidis in [13] added the term sgn(q e4 )
so that the spacecraft maneuver follows the shortest path and
requires the least amount of control torque.

B. Control Law Design

Based on the selected sliding surface proposed in the above
equation, a variable structure attitude controller for the
complete system is presented.

Le/ =r 1 [S(a)% b - a> c 2 )(l(a> b ob - a> c 2 ) + AI s oj s )] -

S(co b ob )

co c 2 , b = —J A, and d = J T e

Equation (2) can be rewritten as:
d> = f + bu + d

(7)

Based on the SMC methodology [1, 2] the control signal
u(t) is defined in (8).

u(t) = u eq (t) + u d (t)

(8)

The first component of the proposed controller is u eq =
[u eql u eq2 u e <;3] r which make sliding surface s(t) invariant
and it is calculated by setting s'(t) to zero considering s(t) to
be zero. Second component u d — [u d i u d2 u d3 ] T is an
extra control effort which forces the state trajectory to reach
on sliding surface in finite time in spite of disturbances and

s - d) + c sgn(q e4 ) q ev =
Substitute with Eq. (7) get:
bu eq = -f-c sgn(q e4 )q 17e

(9)

(10)

Outside the sliding manifold the system trajectory must be
moving towards it. The component u d , is added to satisfy the
reaching condition. For this purpose, the constant rate reaching
law (s = — k sgn(s) is used to specify the dynamics of the
switching function directly [2] the control law is obtained.

bu = -f - c sgn(q e4 )q„ e - ksgn(s) (11)

Substitute the expressions of b,f, and q ve

u(t) = JA- 1 [-S(a> b ob - a> c 2 )(l(a> b b - a> c 2 ) + I s a> s )] +
S(a> ob )a> c 2 -
i/A- 1 csgn(q e4 )(c/ 4e / + [q ve x])a> e - ] A~ 1 ksgri{s) (12)

The controller is simulated as in block (4) and connected in
closed-loop with the nonlinear plant, its effectiveness is
demonstrated through simulations results.

V. The Controller Parameters Selection Approach

In the previous works, the controller parameters (k, c)
were selected by trial and error method that takes more effort
and waste of time. This research presents a novel
mathematical method to select these parameters.

A. A Self-Tuning Approach for Feedback Gain Vector k

The parameter k must be selected to be large enough to
guarantee that the trajectories are reaching and remaining on
the sliding surface in finite time. In other words, k must verify
the following reaching condition [11].

1 d T ^ II

— s s < —ri\s\

1 rl+ III

(13)

Where, n is a strictly positive real constant that determines
the convergence velocity of the trajectory to the sliding
surface. Differentiate (13), then substitute with the control
signal in (11) it gives a general condition on the range of k
values.

s T s < —r]\s\

s T (d>+ csgn(q e4 )q ev ) < -r]\s\

s T (f + bu + d + c sgn(q e4 ) q ev ) < -??|s|

s T (f -f~ c sgn(q e4 )£jW - ksgn(s) +d +

csgn(q e4 ) q ev ) < -T]\s\
s T (d — k sgn (s)) < — r]\s\

\s\d-k\s\ <-rf\s\

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

k > \d max \ +v

To compute the lower limit of k, r\ must be firstly
determined. By integrating (13) between t= and t = tr the
following equation can be obtained:

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
(14) surface s=0 will be reached in some finite time. Once s=0 is

reached, the trajectory in error state-space that slides on the
sliding manifold can be shown to be asymptotically stable
using again Lyapunov's direct method. Another candidate
Lyapunov function is proposed in (23).

s(t = tr) - s(t = 0) < -r](tr - 0)

(15)

Let t r be the time required to hit the surface s(t — t r ) =
and assume the initial value of switching function, s(t=0) =
s(0) > hence

< ^ thus

(16)

If the system initial error is far from the sliding surface, the
system takes longer time to reach the sliding surface and vice-
versa. Then t r is selected such that it is proportional to the
difference between initial and required attitude angles (E diff ) in
deg.

const. E tt

(17)

According to the const. value the reaching phase time can
be increased or decreased, let const. = 1 . Therefore r\ can be
determined from (16), then the lower limit of k for any initial
orientation is computed in (18).

\d n

5(0)
Ediff

(18)

Selection of Sliding Surface Slope(c) Subject to Input
Signal Constant

The system performance is sensitive to the sliding surface
slope c. In (11) the larger values of c, the larger control effort
u(t) then the system will give a fast response, but the reaction
wheels may enter their saturation regions. Now consider the
situation when u max is the maximum admissible value of the
reaction wheel torque. It means that the following inequality
must be hold.

KOI ^"ri

(19)

From analysis of the SMC, the maximum value of the
control signal u(t), is occurred at the initial time such that the
error is maximum. Substitute by the initial value of u(t=0) =
u max and the calculated value of k from Eq.(18). The parameter
c can be obtained from (20)

(«77

-Kf(0)+sgn(s)\d max \))E diff -Jsgn(s)a>(0)

]sgn(q e4 )q ve E diff +]sgn(s)q(0)

(20)

VI. Controller Convergence analysis

To analyze the stability of the system, consider a
positive definite Lyapunov function of the form

V(s)
Then

i T
-s s

V — s T [d — ksign(s)] = d s — k\s\

(21)

(22)

According to the previous selection of k, the derivative of
Lyapunov function is negative, which implies that the sliding

v 2 (q ve ) = - qLqv

Clve = --C WeA Ive + 'Mve X K~C <lve)

(23)
(24)

Substituting Eq. (24) into the derivative of the equation
(23) leads to the following expression

V 2 = - - c

2 2

Qe4\ Qve Qv

(25)

This is clearly negative definite provided c > 0. This
results show that attitude tracking error q ve -» 3xl as t -» oo,
and since the motion is on the sliding surface defined by
s(t) = 3xl It follows that a)(t) -* 3xl as t -* oo.

VII. Chattering Avoidance

In SMC, the control signal may cause an undesirable
chattering phenomenon due to the existence of the sign
function. To alleviate such undesirable performance, the sign
function can be simply replaced by the saturation function
sat{s/e) (as shown in (26) and Fig. 3),

sat(si , e ; ) =

i,-l

i = 1,2,3

(26)

k sat(s/e)

1 1
k

Figure 3: Saturation function

The system is now no longer forced to stay in the sliding
mode but is constrained within the sliding layer|s;| < e t . The
cost of such substitution is a reduction in the accuracy of the
desired performance.

In this work, a constant rate of decay of s is selected to
avoid chattering and e is taken proportional to k.

VIII. Numerical Simulation Resultes

The effectiveness of the proposed control law is
demonstrated by an example of a rest to rest maneuver.
Consider a rigid spacecraft with the inertia matrix I = [14.28
0; 15.74 0; 12.5] kg-m 2 and the inertia matrix of
reaction wheels is Is = [0.002 0; 0.002 0; 0.002] kg-m
2 . The initial attitude orientation of the unit-quaternion is q(0)
= [0.1603, -0.1431, 0.06252, 0.9746] T (which is equivalent to
initial Euler angles E(0) = [20 -15 10] deg.) , the initial value
of the angular velocity is co(0) = [0, 0, 0] r rad/s and the control
authority is assumed to be u max =[0.02 0.02 0.02] T N.m, and the
disturbances are bounded by d max = [0.0001 0.0001 0.0001]

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

N.m. The SMC algorithm computes the suitable controller
parameters required to steer the satellite from initial attitude
condition to zero states in 100 sec with steady state error =
[.0004592 .0005464 .0003582], k = [0.001401 0.001271
0.0016] , c = [0.1748 0.1332 0.256] and e = O.lfc .

A. Comparative Study

To validate the developed control law, the results are
compared with a conventional PD feedback controller. A
simulator of a quaternion based feedback controller is also
built. The quaternion error and angular rate error vectors are
fed to the quaternion feedback controller to generate the
control torque vector.

u(t) = —k p q e — k d a>

(27)

Where k p and k d are the PD controller gains. Notice that,
PD controller has six unknown gains to be selected to meet the
control system requirement.

Simulation studies have been performed to test both
controllers. Figs. 4 and 5 clearly show the performance of the
SMC and PD controller. From figures, the SMC controller
follows the reference angles with little or no overshoot but the
PD controller shows high over and under shoot which is not
acceptable for satellite stable operation. In addition, the
control effort in PD controller is not within the actuator
saturation limit. Moreover, the selection of the controller gains
vectors (k p , k d ) are difficult and tedious. The steady state
error of SMC is lower than PD control (PD steady state errors
are [.01439 .01146 .007685]). Finally, Fig. 6 shows the phase
portraits of the error state-space in SMC & PD controllers.

IX. Conclusion

In this work the problem of tracking a desired spacecraft
attitude in the presence of environmental disturbances and
control input saturation has been justified and solved by means
of a nonlinear controller. A simulator of attitude dynamics of a
rigid satellite actuated via reaction wheels was built using
MATLAB/Simulink software, and its detailed was described.

The developed control algorithm based on Sliding Mode
Control has the following features: (i) Fast and accurate
response in the presence of bounded disturbances; (ii) Explicit
accounting for control input saturation; (iii) Computational
simplicity and straightforward tuning.

The stability based on Lyapunov-like analysis and the
properties of the quaternion representation of spacecraft
dynamics was proofed; and the global stability of the overall
system is guaranteed in the presence of bounded disturbances.
Unlike the early published works, which use trial and error
method for controller parameters determination, the developed
control algorithm presented a novel mathematical way for
sliding mode control parameters computing at every initial
attitude state with promising accurate and very fast
computation.

Moreover the developed control law reduces the
undesirable chattering effect by taking the boundary layer
thickness related to the feedback gain value; and save the
controller energy by taking into account the shortest angular
path maneuvers.

References

[I] A Bastoszewicz and J. zuk, "sliding Mode Control-Basic Concepts and
Current Trends," Inst, of autom. Control, Tech. Univ. of Lodz, Poland
Industrial Electronics (ISIE), IEEE International Symposium on July
2010.

[2] J. Y. Hung, W. Gao, and J. C. Hung, "Variable Structure Control: A
Survey," IEEE transactions on industrial electronics, vol. 40, no. 1,
Febrauary 1993.

[3] A. Bartoszewicz, "Variable Structure Control - from Priciples to
Applications." International Symposium on System Theory,
Automation, Robotics, Computers, Informatics, Electronics and
Instrumentation, Craiova, Romania, 18-20 October 2007.

[4] C. Li, Y. Wang, L. Xu, and Z. Zhang, "Spacecraft Attitude Stabilization
Using Optimal Sliding Mode Control", National Natural Science
Foundation of China 2010 IEEE.

[5] R. Doruk "Nonlinear Controller Design For a Reaction Wheel Actuated
Observatory satellite", a thesis submitted to the graduate school of
applied science of middle east technical university 2008.

[6] J. R. Wertz, Spacecraft Attitude Determination and Control. Academic
Publishers, Dordrecht, Boston, London, 1991.

[7] M. J. Sidi, Spacecraft Dynamics and Control. Israel Aircraft Industries
Ltd, Cambridge University Press 1997.

[8] Q. Hum, L. Xie, and Y. Wang, "Sliding Mode Attitude and Vibration
Control of Flexible Spacecraft with Actuator Dynamics", 2007 IEEE
International Conference on Control and Automation WeDl-2
Guangzhou, CHINA - May 30 to June 1, 2007

[9] C. Pukdeboon, A. S. I. Zinober, and M.-W. L. Thein "Quasi-Continuous
Higher-Order Sliding Mode Controller Designs for Spacecraft Attitude
Tracking Maneuvers", 2008 IEEE

[10] Y. Xia, Z. Zhu, M. Fu, and S. Wang, "Attitude Tracking of Rigid
Spacecraft With Bounded Disturbances", IEEE Transactions On
Industrial Electronics, Vol. 58, No. 2, pp. 647-659, February 201 1

[II] J. Slotine and W. Li, Applied Nonlinear Control. Prentice Hall, New
York, 1991.

[12] M. Jafarboland, N. Sadati, and H. Momeni, "Robust Tracking Control
of Attitude Satellite with Using New SMC and EKF for Large
Maneuvers", IEEEAC paper #1022, Version 5, Updated October 31,
2005

[13] J. L. Crassidis, S. R. Vadali, and F. L. Markley, "Optimal Tracking of
Spacecraft Using Variable-Structure Control," Proceedings of the Flight
Mechanics/Estimation TheorySymposium, NASA Conference, pp. 201-
214 May 1999.

[14] O Hegrenres, J.T. Gravdahl, and P. Tondel, "Attitude Control bY Means
of Explicit Model Predictive Control, via Multi-Parametric Quadratic
Programming.", American Control Conference, Dept. of Eng. Cybern.,
Norwegian Univ. of Sci. & Technol., Trondheim, Norway, pp 901-906,
June 2005.

[15] J. GieBelmann, "Development of an Active Magnetic Attitude
Determination and Control System for Picosatellites on highly inclined
circular Low Earth Orbits," Master of Engineering RMIT University,
June 2006.

[16] Z. Mrozek and S. Tarasiewicz, "Attempting sliding mode controller to
mobile robot arc welding process.," paper presented on III Krajowa
Konf. Metody i Systemy Komputerowe, pp 369-373, Nov. 19-21. 2001.

[17] F. C. Sun, Z. Q. Sun, and G. Feng, "An Adaptive Fuzzy Controller
Based on Sliding Mode for Robot Manipulators", IEEE Transactions On
Systems, Man, And Cybernetics — Part B: Cybernetics, Vol. 29, No. 4,
pp 661-667, August 1999.

[18] M. Reza, M. Hadi, A. J. Koshkouei, S. Effati, "Embedding-Based
Sliding Mode Control for Linear Time Varying Systems", Applied
Mathematics, pp 487-495, 201 1.

AUTHORS PROFILE

Eng. Azza El-Sayed Ibrahim is a PhD student in Computers and Systems
Department at the Electronics Research Institute, Cairo, Egypt. She received
her Master degree from Faculty of Engineering, Cairo University. Research
interest: fault detection and diagnosis in robotic systems, Neural Networks,
and satellite attitude control. azza@eri.sci.eg

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

□ 5

— X

— '

50 100

rf"

200 250 300

I 1 -0.005

-0.01

; \

: \

\ff

^g.....

0.02
0.015

ft*

□fb

Figure 4. Attitude , body angular velocity and the control signal response in case of SMC Controller

□ 5

— X

— '

I
I

50 100

200 250 300

0.02
0.015

-0.015
-0.02

ft""

1 *•

lit.

Figure5. Attitude , angular velocity and the control signal response in case of PD Controller

^ ^ ~ we2-qe2

1
1

)

s \-

m -o.i

° ::

•"""

—

-«..

■^,.

^ ~ we2-qe2

X N

we3-qe3

J 'V

-0.8 -0.6 -0.4

0.2 0.4 0.6 0.8

Figure. 6: a) Phase portraits coe vs qe in SMC

b) Phase portraits coe vs qe in PD

Dr. A. Tobal is an Associate Prof, at the Electronics Research Institute,
Cairo, Egypt. He received his B.Sc, M. Sc. And Ph. D. from Faculty of
Engineering, Cairo University in 1990, 1994 and 1999 respectively. His
fields of research include embedded systems implementation, digital signal
processing, Biological Neural Network, pattern recognition and Artificial
Intelligence. Dr. Ahmed actively participated in the design, implementation
and commissioning of the first Egyptian remote sensing satellite "MisrSat
1" launched successfully in 17/04/2007. toba!5 1 000 @ yahoo.com

as Assistant Professor, Associate Professor and Professor of Control
Engineering in 1999 at the faculty of Engineering in Cairo University. His
research interests include stochastic control, self- tuning and Predictive
control, msultan@cu.edu. eg

Dr. Mohamed A. Sultan obtained his B.Sc. with honors in Electronics
Engineering in 1979. He obtained his M.Sc. and Ph.D. in Control
Engineering in 1982 and 1987 respectively. Since 1987 he was appointed

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 6, June 2012

The Evaluation of Performance in Flow Label and
Non Flow Label Approach based on IPv6

technology

1st Nevila Xoxa Resulaj

Albanian Academy of Science,

Tirane, Albania

nresulaj @ yahoo.com

2nd Nevila Baci Kadzadej

University of Tirana, Faculty of

Economic, Mathematics, Statistics and

Applied Informatics Department,

Tirana, Albania

nevila.baci@ unitir.edu. al

3rd Igli Tafa
Polytechnic University of Tirana Faculty
of Information Technology,
Computer Engineering Department,
Tirana,Albania
itafaj @ gmail.com

Abstract - In this paper, we want to evaluate the performance
of two broadcasters with Flow label and Non flow label
approach. Experimentally we have presented that the
throughput utilization for each broadcaster with Flow Label
approach which is implemented in MPLS Routing Technology
is 89,95%. This result is better than Non Flow Label approach
which is evaluated at 92,77%. The aim of this paper is to
present that MPLS Routers performance is better than ff
routers especially in Throughput Utilization, Low Level of
Drop Packet Rate and time delay. The second technology is
implemented in ff routing. Experimentally we have generated
some video stream packets between 2 broadcasters with an
arrange of router nodes. Experiments are performed by using
ns-2 simulator.

Keywords-MPLS technology, IP routing technology,
Throughput, Flow-Label approach, ns-2 simulator

1. INTRODUCTION

As we know IPv6 is a recent technology of
communication and it gives a lot of improvements
compared to IPv4 [5], [2], [3]. These improvements based
on features upgraded by the Internet Engineering Task
Force (IETF), for example, the increase of the address
space from 32 bits to 128 bits or the increase of some
significant QoS conditions. By using the recent multimedia
applications technologies [7], internet providers,
companies, subscribers and the researchers will take some
benefits. The Internet Protocol (IP) is considered to be a
best effort service, so in the future, the TV broadcasters
will use the IP address for communication. In other words,
there will be a convergence of the broadcast network with
the IP to form the Internet Protocol Television (multimedia
with IP) under the recent development.

There are built some policies based on flow-labels to

manage
the routing of the packets (channels) to the nodes

(subscribers)
during the transmission with IP-multimedia approach.

For example, a broadcaster can tend to utilize the full

bandwidth from the network manager, but meanwhile

the network manager asks fairness in distributing
packets to the remaining broadcasters [5], [6]. As it
know throughput is one of the important feature of
QoS Routing, because the management of throughput
offers a better QoS performance. It is interesting to
mention that IPv6 not only overcomes the shortcoming
problems in the IPv4, but also it takes the benefits in
Quality of service (QoS). QoS in IPv6 plays an
important role in the Stream Model Approach between
broadcasters [1], [4]. In [3] the packet's traffic on
channel is organized without flow label technology.
Flow label technology means that instead of router
nodes (fig 1) based on IP routing we can use MPLS
routers. MPLS technology has some advantages, but
the most one is speed routing. Based on some executed
tests we can present that bandwidth utilization is
another good feature compared with IP routers
technology.

The objective of this paper is to highlight our
simulation results in terms of two attributes which are
the Throughput and Time Computation Performance
based on IPv6 technology with flow label packets
technology in Multi-channel Stream Approach. Than
we want to compare the results of our simulation with
non-flow label packets technology in Multi-channel
Stream Approach.

The rest of the paper is organized as follows: section 2
briefly discusses the comparison between MPLS and
IP routing section 3 presents the experimental analysis
and results, in section 4 are given some conclusions
and future works and finally are presented the
references.

2. COMPARISON BETWEEN MPLS ROUTING
AND IP ROUTING.

1. IP routing uses hop-by- hop destination-only forwarding
paradigm. When forwarding IP packets, each router in
the path has to look up the packet's destination IP

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 6, June 2012

address in the IP routing table and forward the packet to

the next-hop router. [8]

MPLS uses a variety of protocols to establish Label

Switched Paths (LSP) across the network. LSPs are

almost like Frame Relay or Asynchronous Transfer

Mode (ATM) permanent virtual circuit (PVC), with two

major differences: they are unidirectional and they can

merge (all LSPs toward the same egress router could

merge somewhere in the network). One of the protocols

[8]

MPLS is faster than IP routing because it is based on

label

MPLS is in 2,5 OSI Layer and IP is in 2 OSI Layer.

we have performed experiments with router nodes which
are based on IP technology (non flow label technique). We
have repeated this experiment with MPLS routers (flow
label technique).

3. EXPERIMENTAL ANALYSIS DESIGN AND
RESULTS

In this section, we want to test the Throughput and Time
Delay based on IPv6 technology with non flow label
packets technology and flow label packets technology in
Multi-channel Stream Approach. As we presented above
we have used IPv6 technology because it offers more
flexibility and QoS features than IPv4

3.1 Experimental Analysis

In the Multi - Stream Approach we have tested up to 10
nodes for 2 broadcasters as end-users. We have used ns2
simulator since it is considered to be powerful, efficient and
flexible for simulation. The 10 nodes were tested
sequentially starting from 1 node, 2 nodes, 3 nodes, ... , 10
nodes, respectively. We have simulated for both
broadcasters Video Stream Packets with 1 .4 KB packet
length, Rate Video Stream is 1.5 MB/sec and Bandwidth is
5 MB. Network topology is BUS. In NS2 simulator we
configure RIP version 2 Routing Policy. We have choosen
approximately characteristics with real environment [3].
The maximum Video Packet supported by Maximum
Transmission Units (MTUs), which include the Maximum
Segment Size (MSS) plus the 40-byte header, within
TCP/IP traffic. We'd like video packets (which include a
smaller header, apparently) to be around 1400 bytes to fit
within acceptable limits and eliminate the possibility of
broken packets.

Initially, the first broadcaster generate video stream packets
to second one by httperf tool. In the first broadcaster we
have installed client machine and in the second one we
have installed server machine. In server machine we have
built Apache Web Server. So the client is sending video
packet request by using http protocol to the server machine.
On the other hand second broadcaster can generate http
video request to the first one. At this moment client
machine is transform in server machine and vice versa.
Thus at the same time one machine will utilized as client
and server by installed Apache Web Server (Apache2 on
CENTOS 5.5 OS)

For every experimental phase (by 2, 3 ,4 ...10 nodes), we
have calculated the throughput , then we have compared the
throughput of the nodes into both broadcasters. Previously

FIGURE 1. Two broadcasters and 4 nodes. The broadcasters generate
video packet traffic between nodes

As it look from figure 1 two broadcaster generate video-
stream packets at the same time. All these packets are
routing on these nodes based on RIP v2 policy.

In [3] the throughput for a determined broadcaster and the
number of nodes is calculated as in the following
equation:

Num(SBW)-Num(RBW) tnnm

Throughput = -xl00% (1)

Num(SBW)

Throughput: The amount of the non-lost received

bandwidth.

Num. (SBW): The amount of the bandwidth provided by

the network manager. Packets should be sent to all nodes

of the determined broadcaster.

Num. (RBW): The amount of the bandwidth that is

received from the determined broadcaster. This amount

should get different value than SBW, because some packets

have to lost during routing.

3.2 Simulation Results

In order to evaluate our method, the main attribute is the
Throughput between the nodes and their broadcasters. We
did compare the throughput behavior of each broadcaster
with their nodes starting from 1 node and increasing the
size to 10 nodes, based on IP routing protocol. The
experiment presents that the total throughput for the 2
broadcasters with 10 nodes with IP routing technology is
92.77% . If we use the Non-Flow Label Technique which
means that we can replace the IP routers with MPLS
routers, with the same policy routing (RIP) with 2
broadcasters which generate the same packet traffic, the
total throughput utilization for each broadcaster is decrease
to 89,95%. This means that one broadcaster can use the
same number of video stream packet generated with
smaller utilization bandwidth. All router nodes in figure 1
are configured with IPv6 address. The total number of
packets generated from each broadcaster is 1000. As it
looks from table 1 and table 2, if the number of nodes is

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 6, June 2012

increased the total throughput utilization for each
broadcaster is decreased linearly. The number of dropped
packets increased linearly if the number of nodes increased
too (table 3,4). Each node can introduce drop packets (the
reason are buffer, architecture of routers etc). In this paper
we compared the percentage of dropped packets and time
delay between 2 technologies, non-flow labels packet and
flow labels packet as it shows in table 3-6.

TABLE 1. The throughput results for each broadcaster and a defined
number of nodes without flow labels technology (IP)

TABLE 4: The percentage of dropped packets between 2 broadcasters and
nodes with flow label packet (MPLS)

Nr of Nodes Throughput

94.401%

94.227%

94.055%

93.901%

93.607%

93.414%

93.243%

93.134%

92.998%

92.777%

TABLE 2: The throughput results between 2 broadcasters and number of
nodes with flow labels technology (MPLS)

Nr of Nodes Throughput

91.015 %

91.012 %

91.007 %

91.004 %

90.452 %

89.970 %

89.967 %

89.961 %

89.960 %

89.957 %

TABLE 3: The percentage of dropped packets between 2 broadcasters and
nodes with non-flow label packet (IP)

Nr of Nodes Drop Packets

1.025 %

1.142 %

1.272 %

1.444 %

1.652 %

1.876 %

2.067 %

2.261 %

2480 %

2.631 %

Nr of Nodes Drop Packets

1.024 %

1.140 %

1.271 %

1.441 %

1.652 %

1.875 %

2.067 %

2.260 %

2.480 %

2.630 %

TABLE 5: Time delay in MS Approach with non-flow label packet (IP)

Nr of Nodes Time delay

2,16 ms

3,44 ms

5,99 ms

832 ms

9,99 ms

11,39 ms

14,22 ms

17,86 ms

21,62 ms

26,55 ms

TABLE 6: Time delay in Multi-Stream Approach with -flow label packet
(MPLS)

Nr of Nodes Time delay

1,66 ms

2,56 ms

3,77 ms

6,20 ms

8,52 ms

9,98 ms

11,04 ms

12,56 ms

14,24 ms

14,89 ms

We have presented graphically, throughput utilization and
time delay (figure 2 and figure 3) based on the flow-label
technology. In figure 3 time delay increases linearly when
the number of nodes increased too, because each router
nodes introduce a slight delay. In figure 2 throughput
utilization is decreased when the numbers of nodes is

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 6, June 2012

increased. As we mentioned above the reason is increasing
of data rate lost for each node. We have a sensitive
reduction of throughput utilization, between node 4 and
node 6. This was happen because in those nodes the ratio of
drop packets is bigger than 3 nodes. After 6 nodes the drops
of packet are stabilized.

Throughput Utilization

91.5

91
90.5

90
89.5

>>»»

'Throughput
Utilization

FIGURE 2. Throughput results between 2 Broadcasters.

Time Delay

FIGURE 3. Time delay between 2 Broadcasters.

4.CONCLUSIONS AND DISCUSSIONS

1 . As it look from table 3 and table 4 the drop packets
rate are similarity for both methods (flow label and
non-flow label). This is because both routers have the
same buffers, so it doesn't affect the performance of
drop packets routing.

2. If we compare table 5 and table 6 the difference of
time is visible. This is because MPLS routers
characterized from a fast routing technology. The
reason is routing packet which are based on labels, not
in IP. This is an important feature of the best
throughput utilization in flow label technology,
descripted in table 2 compared with non-flow label
technology in table 1 .

In the future we will increase the number of broadcasters
and routers. Also we will generate the dynamic length of
video stream packets in order to evaluate the throughput
utilization performance and time delay in WAN

5. REFERENCES

[1] Almadi M.A, Idrus R, Ramadass S, Budiarto R, "A Proposed

Model for Policy-Based Routing Rules in the IPv6 Offering
QoS for IPTV Broadcasting," International Journal of
Computer Science and Network Security, IJCSNS, VOL.8
No.3, March 2004, pp. 163-173, 2008.

[2] Cho K, Luckie M, Huffaker B, "Identifying IPv6 Network

Problems in the Dual-Stack World" In Proceedings of the
Annual Conference of the Special Interest Group on Data
Communication, SIGCOMM'04, Portland, Oregon, USA, 30
August- 3 September 2004.

[3] Liang, J, Yu B, Yang Z, Nahrstedt K.. "A Framework for

Future Internet-Based TV Broadcasting," In Proceedings of the
International World Wide Web Conference, multimedia with
IP Workshop, Edinburgh, Scotland, United Kingdom, 2006

[4] Pezaros DP and. Hutchison D. "Quality of Service Assurance

for the next Generation Internet," In Proceedings of the 2nd
Postgraduate Symposium in Networking, Telecommunications
and Broadcasting (PGNet'01), Liverpool, UK, June 18-19,
2001.

[5] Pezaros D.P, Hutchison D, Gardner R.D, Garcia F.J and

Sventek J.S, "Inline Measurements: A Native Measurement
Technique for IPv6 Networks," In Proceedings of the
International Conference of the IEEE for Networking and
Communication, pp. 105-110, 2004.
[6] Silva J. S, Duarte S, Veiga N, and Boavida F,"MEDIA - An

approach to an efficient integration of IPv6 and ATM multicast
environments," [Online]. Available:

http://cisuc.dei.uc.pt/dlfile.php?fn=171_pub_SaSilva.pdf&get=
l&idp=171&ext= April 12, 2008.

[7] Zhiwei Y, Guizhong L, Rui S, Qing Zh, Xiaoming Ch, Lishui

Ch. "School of Electronics and Information Engineering Xi'an
Jiaotong University, Xi'an, China 710049, "A Simulation
Mechanism for Video Delivery Researches, 2009

[8] http://searchtelecom.techtarget.com/answer/What-is-the-

difference-between-MPLS-and-normal-IP

AUTHORS PROFILE

Nevila XOXA RESULAJ, studied Computer Science at the Faculty of
Natural Sciences where she obtained her MSc. in 2001 and since March
2012 is a student of Ph.D. at this Faculty. She is ICT Administrator at
Academy of Sciences of Albania, part-time pedagogue at Polytechnic
University of Tirana for the course "Fundamentals of programming in C",
part-time pedagogue at University of Tirana, Faculty of Economy for the
course "Informatics" and part-time pedagogue at University of Tirana,
Faculty of Natural Sciences, Department of Informatics for the course
"Computer organization" and the course "Introduction to Informatics".

Nevila BACI KADZADEJ, is prof.assoc since September 2011, at
University of Tirana, Faculty of Economy, Department of Mathematics -
Statistics - Applied Informatics. Also she is Research associate at Centre
for Research and Development Tirana. Since 2001 attached to this
institute as statistic expert in implementation of conducting business
surveys (manufacturing and construction sector) with EU methodology in
Albania. She is member of some important projects in Albanian such as:
Assessment of business constrains in manufacturing sector, Administrative
constrains in construction sector, Assessment of micro enterprises activity
in Albania, Strengthening institutional capacities of NPO in Shkodra
region for improvement the social assistance scheme, etc.

Igli TAFA, is PhD Student at Polytechnic University of Tirana, Computer
Engineering Department. Since 2003 he is assistant pedagogue at this
Department. He has finished the Master Degree at 2007. Also he has
participate in some projects such as: See Grid, FP7 etc.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

False Colour Composite Combination Based on the
Determinant of Eigen Matrix

Maha Abdul-Rhman Hasso

Department of Computer Science, College of Computer Sciences and Math.

University of Mosul / Mosul, Iraq

Maha_hasso@vahoo , com

Abstract In remote sensing applications, a wise

selection of the best colour composite images out of many
possible combinations is necessary to ease the job of the
interpreter and to overcome the data redundancy problem.

This work includes a novel method for ranking the
available three-band combinations according to the amount of
information they contain. The method is based on measuring the
determinant of the variance/covariance matrices of each possible
combination. The consistency of the method is described and
proven using the Eigen value matrix. However for ranking
calculation the variance/covariance matrix is enough.

I. Introduction

In most of the remote sensing application colour
composite image represent an important stage in the whole
process of information extraction [1]. Due to the fact that
most of the available sensors on board the current satellite
portray the earth surface in more than three bands, the
selection of the most important combination becomes a crucial
matter in any application.

In the case of MSS sensor six different combinations of
false colour composite can be made out of the four available
bands. Whereas in the TM-sensor case thirty-five
combinations can be made out of the seven bands of the
sensor.

Given the fact that the spectral resolution of the future
sensors in expected to be increased.

This number of combinations is expected to be increased
for the next generation of the sensors which are expected to
contain more than seven bands.

For image interpreter, dealing with that number of false
colour composite images is a difficult task. Therefore, the
selection of the most informative combination becomes a
necessary step prior to any image analysis and interpretation.
This work is involves the introduction of new method for
ranking the available combination according to the amount of
information (colours) that they may contain.

II. Concepts of colours space

Colours space is a three dimensional feature space whose
axes are represented by the three primary colours RGB. In
generating colour composite images, the gray levels of each
image are projected on one of these axes. The resultant
distribution shows the range of colours that can be generated.

The more space the distribution occupies the more range
of colours that can be generated.

Multispectral images taken by satellite sensors show a
substantial degree of correlation. This is partly due to the
natural similarity between the spectral characteristics of the
cover types and partly it is due to the limitation of the spatial
and spectral resolution of the satellite sensors.

The length of each principal axes represent the square
root of the data variance along that particular axis [1]. Thus, if
the data distribution is rotated (linearly transformed) using
principal component transformation, the resultant eigen value
matrix will show the diagonal element as the variance of the
output component and the off diagonal, which are zeros, as the
covariance between the output component. Thus, the more
close these diagonal element, are the more scattering of the
data will be along the direction of the second & third principal
component axes. Thus the volume of the distribution can be
calculated simply by multiply the three diagonal axes. For
example, an eigen value matrix with the diagonal elements of
8, 4 and 2 will produce a volume of 64 while if these eigen
value are changed 7, 4, and 3 with their sum remaining the
same, the resultant volume will be 84.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Figure (1), the separation axis between each two bands. X t , X 2
the bands, /j is the mean and PCs are the principle component axis

III. Previous methods

Several strategies and methods exist for selecting the
combinations that suits the application in hand. Some of them
are based on the spectral characteristics of the existent cover
types and others are based on some statistical measurements
of the images.

In applications that motivate mapping or separation of
specific cover type such as mineral exploration and vegetation
mapping, the selection of the bands is made according to the
spectral characteristics of the mineral indictors and vegetation
[7], For instance vegetation shows high reflectance at the near
infrared band while iron oxide, which is a good indicator of
the mineral prospected areas, shows an absorption feature at
the same band. Thus, the inclusion of bands in the colour
composite combination may be useful for discriminating these
two cover types. However, in applications that motivate
general mapping of the existence cover types the selection of
the bands is made according to their position in the spectrum.
For instance, one band from the visible region, one band from
the near infrared region and one band from the middle or
thermal infrared region are selected. This strategy is justified
by the fact that naturally the spectral response of a particular
cover type, more likely, shows more variation between two
distinct bands than between two adjacent bands. Accordingly,
for TM sensor bands 147 may be chosen.

In addition to these strategies several methods based on
statistical properties of the available bands were introduced.
One of these methods takes into account the degree of
correlation between the possible pairs of the available bands
and the best combination is chosen by selecting the bands that
show least correlation [3-4], However, this method does not
take into account the variance of the available bands. That is,
bands with high variance (more information) may be
discarded.

The other method is based on the variance of the
available bands. That is bands which show high variances are
selected. This method again shows a limitation since it does
not take into account the degree of correlation between bands.
Thus there will be information redundancy. That is, bands
with high variance that show high degree may be selected.
According to Figure (1) this will result a colour composite
image with law saturation, i.e. few colours, each with more
different shades (narrow range of colours). Chavez (1983)
introduced a method that takes into account both, the degree
of correlation between the bands and the variance of the bands
[2]. That was the calculation of The Optimum Index Factor

(OIF) to select a three-band combination that displays the
greatest details among a maximum of 20 bands by formulating
the following equation:

3 3

oif =Y J sD i/Y) cCj i

i=l

;'=i

Where SDi is the standard deviation of band i and

CCj is the absolute value of the correlation coefficient
between any two of the possible three pairs. According to
Chavez et al., the highest values of OIF should be the three
bands having the most information content. This measure
favours the selection of those bands having high variances and
low pair- wise correlation [5].

IV. Present method, The Determinant Based method

Referring to Figure (1), it is clear that the more the three
axes of the ellipsoid are close to each other the more
scattering of the distribution will be across its diagonal. Thus,
more colours will be generated.

In most of the remote sensing study, the distribution of
the data is assumed Gaussian or near Gaussian. Thus, the axes
of the ellipsoid will represent the three principle axes of the
distribution.

This proofs, that the more close the eigen value are the
more space the distribution occupy and accordingly a better
colourful image can be produced. The multiplication of the
three diagonal element of the eigen value matrix is equivalent
to the determinant of the variance/covariance matrix [6]. Since
the shape of the distribution is invariant under rotation. That is,
the determinant of the covariance matrix is positive, i.e.,

Det(C x ) = Y\ h *

i=l

The eigenvectors of the covariance matrix transform the
random vector into statistically uncorrelated random variables,
i.e., into a random vector with a diagonal covariance matrix.

V. Application to TM images

The given method is applied to a multispectral images of
TM-sensor, thermal bands is excluded. Twenty combinations
can be making out of six remaining bands.

The Table (1) shows the ranking and combination of the
applied OIF method and the proposed DET method.

Table (1) shows the ranking of the twenty combination using the OIF
method and determinate method.

Combine

OIF

Band i

Band j

Band k

DET

Band i

Band j

Band k

69.5493

19154201.94

67.4986

11163892.05

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

65.6903

10055252.29

63.1119

6983970.83

60.1420

5969593.84

57.0226

5006852.82

54.5363

4016074.97

53.0176

3877119.68

43.3697

3352550.15

42.3461

3332290.24

29.3038

2346223.25

27.9850

1983992.73

27.4343

1164365.92

25.9559

1126517.91

24.4786

1046195.57

23.9083

718679.35

23.8683

429827.41

20.9303

236031.38

20.4402

215006.70

18.2356

80846.22

From the table above it is clear that the best combination
of false colour composite is bands 3,4,5 that gives the
maximum OID and determinate. Figure (2) shows the false
colour composite of TM-bands to RGB image with the
reversed combinations of bands 3,4,5 on red, green and blue
colours.

(e) (0

Figure (2) Two False colour composite combination of bands 3,4,5 to RGB. a)

bands 3,4,5 ; b) bands 3,5,4; c) bands 4,5,3 ; d) bands 4,3,5 ; e) bands 5,3,4 ;

f) bands 5,4,3.

As shown in the figure (2), the best combination visually
interpreting is when the band 5 is coloured as red, band 4 is
green and band 3 is blue.

VI. Conclusion

Remotely sensed data colouring is important for easy
vision and interpretation. The calculation of the eigen value
matrix is not necessary since the multiplication of the three
diagonal element of the eigen value matrix is equivalent to the
determinant of the variance/covariance matrix. It is clear that
in some cases the OID & DET gets the same combination but
this combination is not the best. The best combination is the
highest value of OID and DET(bands 3,4,5) which means that
these bands has good amount of information with minimum of
data redundancy.

References

[1] C Ayday, E. Gumiisliioglu " Detection And Interpretation Of
Geological Linear Features On The Satellite Images By Using
Gradient Filtering And Principal Component Analysis", The
International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing 2008

[2] Chavez, P.S., Jr., G.L. Berlin, and L.B. Sowers, "Statistical Method
For Selecting Landsat Mss Ratios", Journal of Applied Photographic
Engineering, 8(l):23-30, 1982.

[3] Ehsani, A . H . & Quiel, F. "Efficiency of Landsat ETM+ Thermal
Band for Land Cover Classification of the Biosphere Reserve "Eastern
Carpathians" (Central Europe) Using SMAP and ML Algorithms", Int.
J. Environ. Res., 4(4):741-750 , Autumn 2010

[4] Laurence A. Soderblom, Robert H. Brown, Jason M. Soderblom, Jason
W. Barnes, Randolph L. Kirk.Christophe Sotin, Ralf Jaumann, David J.
Mackinnon, Daniel W. Mackowski, Kevin H. Baines, Bonnie J. Buratti,
Roger N. Clark, Philip D. Nicholson "The geology of Hotel Regio,
Titan: Correlation of Cassini VIMS and RADAR", ilcarus 204 (2009)
pp., 610-618, Published by Elsevier Inc., 2009.

[5] M. Beauchemln and KO B. Fung "On Statistical Band Selection for
Image Visualization", Photogrammetric Engineering & Remote
Sensing Vol. 67, No. 5, pp. 571-574. American Society for
Photogrammetry and Remote Sensing, May 2001.

[6] Michael McCourt, "A Stochastic Simulation for Approximating the
log-Determinant of a Symmetric Positive Definite Matrix" .December
15, 2008, http://www.thefutureofmath.com/mathed/logdet.pdf

[7] Randall B. Smith, "Introduction to Remote Sensing of Environment
(RSE)" , ©Microimages, Inc., 4 January 2012.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Source Initiated Energy Efficient Scheme for
Mobile Ad Hoc Networks

R.Bhuvaneswari
Anna University of Technology, Coimbatore
Coimbatore, India

Dr.M.Viswanathan
Senior Deputy Director, Fluid Control Research Institute (FCRI),
Palakkad, Kerala, India.

Abstract - In Mobile Ad Hoc Networks (MANETs), nodes are
organized in a random manner without any centralized
infrastructure. Due to node mobility and limited bandwidth,
nodes consume more power unnecessarily. Mobile nodes
collects the route information through overhearing and stores
these information in route caches through Dynamic Source
Routing (DSR) Protocol. When the route cache freshness is
absent, it leads to the stale route information resulting in
pollution caches. If the node overhears the packet to another
node, node's energy consumption occurs unnecessarily. The
main goal of this research work is to reduce the effect of
overhearing and avoid the stale route problems while
improving the energy efficiency using the Source Initiated
Energy Efficient (SIEE) algorithm. Due to the lack of route
cache update, the stale route entry and overhearing is
originated among the network. For that, we developed five
mechanisms to improve route cache performance in DSR. By
simulation results the proposed algorithm achieves better
performance than the existing methods.

Keywords - MANET, DSR, Stale route entry, Cache freshness,
overhearing, route cache update and energy efficiency.

I. INTRODUCTION

A. Mobile Ad Hoc Networks (MANET)

Mobile ad hoc network (MANET) is an infrastructure-
less multi-hop network where each node communicates with
other nodes directly or indirectly through intermediate
nodes. Thus, all nodes in a MANET basically function as
mobile routers participating in some routing protocol
required for deciding and maintaining the routes. Since
MANETs are infrastructure-less, self-organizing, rapidly
deployable wireless networks, they are highly suitable for
applications involving special outdoor events,
communications in regions with no wireless infrastructure,
emergencies and natural disasters, and military operations
[!]■

B. Dynamic Source Routing (DSR) Protocol and the effects
of overhearing

Dynamic Source Routing protocol is a simple and
efficient routing protocol designed especially for use in
multi-hop wireless Ad Hoc networks of mobile nodes. DSR
allows network to be completely self-organising and self-
configuring, without the need for any existing infrastructure
or administration [2], DSR gathers the route information
through overhearing. Overhearing improves the routing
efficiency in DSR by eavesdropping other communications
to gather route information but it spends a significant
amount of energy. Overhearing [3] means a node picks up
packets that are destined for other nodes. Wireless nodes
will consume power unnecessarily due to overhearing
transmissions of their neighboring nodes. Wireless nodes
consume power unnecessarily due to overhearing the
transmissions of their neighbors. This is often the case in a
typical broadcast environment. For example, as the IEEE
802.11 wireless protocol defines, receivers remain on and
monitor the common channel all the time. Thus the mobile
nodes receive all packets that hit their receiver antenna.
Such scheme results in significant power consumption
because only a small number of the received packets are
destined to the receiver or needed to be forwarded by the
receiver.

C. Problem of Stale Route in Source Routing

When the link errors [4] (or RERR) are not propagated by
route caches often contain stale route information for an
extended period of time. Including this, the erased stale
routes are possibly un-erased due to in-flight data packets
carrying the stale routes. If a node has an invalid route in its
route cache or receives a Route Reply that contains an
invalid route, it would attempt to transmit a number of data
packets without success while consuming energy. The
design choices for route cache in Dynamic Source Routing
protocol and concluded that there must be a mechanism,

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

such as cache timeout, that efficiently expels stale route
information.

The main cause of the stale route problem is node
mobility. It is unconditional overhearing that dramatically
exaggerates the problem. This is because DSR generates
more than one RREP packets for a route discovery to
offeralternative routes in addition to the primary route to the
source. While the primary route is checked for its validity
during data communication between the source and the
destination, alternative routes may remain in route cache
unchecked even after they become stale. This is the case
notonly for the nodes along the alternative routes, but also
for all their neighbors because of unconditional overhearing.

II. PREVIOUS WORK

Hu C et.al [5] developed the 802.1 1 Power Saving Mode
(PSM) applicable in multihop MANET with Dynamic
Source Routing (DSR) protocol. The drawback in
integrating the DSR protocol with 802.1 1 PSM comes from
unnecessary or unintended overhearing and DSR depends
on broadcast flood of control packets.

Lim S et.al [6] explored a mechanism called
RandomCast mechanism. Here a node may decide not to
overhear i.e. a unicast message and not to forward and a
broadcast message when it receives an advertisement during
an ATIM window, thereby reducing the energy cost without
affecting the network performance. In addition to the energy
consumption, overhearing brings in several undesirable
consequences. It could aggravate the stale route problem,
the main cause of which is node mobility.

Sree Ranga Raju [7] proposed a conservative approach
to gather route information. It does not allow overhearing
and eliminates existing route information using timeout.
This necessitates more RREQ messages which in turn
results in more control overheads in routing.

Laura marie feeney [8] analysed the energy consumption
model for routing protocols in ad hoc networks. They have
also shown that new insights are provided into costly
protocol behaviours and suggests opportunities for
improvement at the protocol and link layers.

Ramesh et.al [9] explored a new scheme called efficient
energy management to achieve minimum energy
consumption with the presence of overhearing. Here they
have proposed five modules like networking module, packet
division module, randomcast module and energy efficient
balancing module in order to avoid redundant rebroadcasts
and thus save additional energy.

Ashish K et.al [10] & Charles E Perkins et.al [11]
proposed the on-demand routing protocols DSR and AODV,
before sending a packet to the destination, discovers a route.
Route maintenance is invoked when node detects link

failure. In order to avoid route discovery for each packet,
on-demand routing protocols utilizes cache routes
previously learnt.

Ashish Shukla [12] proposed a cache timeout policy to
predict route cache lifetime, and to expunge stale route
cache entries, which are timed out. Many techniques have
been proposed for route cache organization and its effect on
the performance of on-demand routing protocols. But the
concentration of cache timeout policy is very less.

It is used in route cache implementation to prevent stale
route from being used. So, a technique for reducing the
unintended overhearing of neighboring nodes is done with
the help of RandomCast mechanism and for prevention of
stale route problem, a cross-layer cache timeout policy is
implemented. Time out policy derives cache timeouts of
individual links that are present in route cache by utilizing
Received Signal Strength Indicator (RSSI) information. So
to fulfil the objective and to overcome the drawbacks, a
message overhearing and forwarding mechanism called
RandomCast [4] is chosen which makes a judicious balance
between energy and network performance.

Sangeetha et.al [13] used the prediction mechanism and
smart prediction mechanism which performs better than
EOLSR protocol and reduce traffic load. In MANET state
information such as residual energy level plays an important
role in route selection. If latest information is not collected by
nodes, then performance may degrade. They have also
evaluated the effect of time at which state information was
collected in ideal and realistic approach and concluded that
although ideal approach is better than realistic but increase in
frequency of packets improve the performance very little and

also increase traffic overhead.

B. H. Liu [14] used hello messages to distribute
transmission power, and uses the minimum power required
to connect to a neighbor, while considering the costs of
reception of a packet at the neighboring nodes.

Freeny [15] suggests that if ATIM window is fixed then
energy saving can be affected. DPSM improves this
performance by using the variable ATIM window. It allows
the sender and receiver node to change their ATIM window

dynamically. The ATIM window size increased when some
packets are pending after the current window is expired. The
data packets carry the current length of the ATIM window
and the nodes overhear this modify their ATIM window
length. DPSM allows the sender and receiver node to switch
of their radio immediately after their transmission is over.
The energy saving performance of DPSM is better as
compare to IEEE 802.11 DCF in term of power saving
however it is computationally more complex.

S.Singh and C.S.Raghavendra [16] projected energy
efficiency technique which is achieved by using two
separate channels, one for control and other for data.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

RTS/CTS signals are transmitted over the control channel
while data are transmitted over data channel. Nodes with
packet to transmit sends a RTS over the control channel, and
waits for CTS, if no CTS they receives within a specific
time then node enters to a backoff state. However, if CTS is
received, then the node transmits the data packet over the
data channel. The receiving node transmits a busy tone over
the control channel for its neighbours indicating that its data
channel is busy. The use of control channel allows nodes to
determine when and how long to power off. The length of
power off time is determined by different condition. After
waking up, a node access the channel over the data channel
and found multiple transmission going on. The node uses a
probe protocol in this case to find how much time it will
power off. Simulation results shows that good range of
power saving is achieved.

In IEEE 802.1 1 [17] standard protocols, it has two types
of power managements. First type is known as power save
(PS) mode for infrastructure based wireless network and the
second type is known as IBSS Power Saving mode, which is
for infrastructure-less networks. In the former method nodes
in PS mode consume less power compare to active mode
operation. The access point buffered the Media
Authentication Code (MAC) service date unit (MSDU) and
transmits them at designated time by the help of Traffic
Indication Map (TIM) and delayed traffic indication map
(DTIM). This type of power saving mechanism is not
suitable for ad hoc network environment as there is no
central coordinator like access point. On the other hand
IBSS PS mode is applicable to fully connected single hop
network where all the nodes are in the radio range to each
other. Synchronized beacon interval is established by the
node which initiates the IBSS and is maintained in a
distributed fashion. All the nodes wake at the beginning of
the beacon interval and wake till the end of the traffic
window. The nodes participating in the traffic
announcement remain awake till the end of beacon interval
and the non-participator goes to sleep to conserve energy at
the end of the traffic window. The amount of energy
conserve by a node depends upon the time spent in the sleep
state which can be affected by the state transition from sleep
to active mode operation. The energy saving performance
also depends upon the network size as well as on the length
of the ATIM window and beacon interval duration.

Sofiane Boukli Hacene et.al [17] improved the promising
DSR routing protocol for ad hoc networks. They have
equipped DSR with expiration time technique for routes in
route cache. This technique has been inspired from route
management in the routing table of Ad Hoc on Demand
Distance Vector (AODV) routing protocol, in order to avoid
the use of stale route in routing. The performance of the
proposed technique was evaluated and compared with DSR
using detailed simulations. Several common performance
metrics were considered. The proposed technique can

overall generate lower communication overhead, fewer
broken links and lower average end-to-end delay.

The paper is organized as follows. The Section 1
describes introduction about MANET, overview of DSR
protocol and stale route problems in DSR. Section 2 deals
with the previous work which is related to the energy
consumption. Section 3 is devoted for the implementation of
source initiated energy efficient algorithm. Section 4
describes the performance analysis and the last section
concludes the work.

III.IMPLEMENTATION OF SOURCE INITIATED
ENERGY EFFICIENT (SIEE) ALGORITHM

In our proposed technique, we have used four
mechanisms in order to avoid stale route problems, achieve
minimum energy consumption. Dynamic Source Routing
aggressively uses route caching. Using source routing, it is
possible to cache every overheard route without causing
loops. If any forwarding node caches any source route in a
packet, it forwards the packets for possible future use. The
destination node replies to all requests. Thus the source
node learns many exchange routes to the destination nodes
that are cached. Swap routes are useful in case the primary
route breaks. If any intermediate node on a route learns
routes to the source and destination as well as other
intermediate nodes on that route. A large amount of routing
information is gathered and cached with just a single query
reply cycle. So these cached routes may be used in replying
to subsequent route queries. The reply from caches provides
dual performance advantages. First, it reduces route
discovery latency and without replies from caches the route
query flood will reach all nodes in the network. Cached
replies satisfy the query flood early, thus saving on routing
overheads.

If without an effective mechanism, stale cache entries are
removed. Then, route replies may carry stale routes.
Attempted data transmissions using stale routes incur
overheads and generate additional error packets and can
potentially pollute other caches when a packet with a stale
route is forwarded or snooped on. In the following, there
are three problems are identified with the DSR protocol that
are the root cause of the stale cache problem.

Case i:

If link breaks and route errors are not propagated to all
caches that has an entry with the broken link. Instead, the
RERR (Route error) is unicast only to the source whose data
packet is accountable for identifying the link crack via a link
layer feedback. We take only a limited number of caches are
cleaned. The failure information is propagated by
piggybacking it onto the subsequent route requests from the
source. If the route requests may not be propagated
network-wide, many caches may remain unclean.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Case ii:

Still now there is no mechanism is proposed to expire stale
routes. If not fresh, stale cache entries will stay forever in
the cache.

Case Hi:

There is no way to determine the freshness of any route
information. For an example, even after a stale cache entry
is erased by a route error, a subsequent "in-flight" data
packet carrying the same stale route can put that entry right
back in. This problem is mixed by liberal use of snooping.
Stale routes are chosen up by any other node overhearing
any transmission. Thus, cache "pollution" can propagate
fairly quickly.

Proposed Approaches:

A. Maximum Error Declaration: The proposed approach is
based on the idea that bad news should be propagated "fast
and wide". In case if we want to increase the speed and we
need to the extent of error propagation, so the route errors
are now transmitted as broadcast packets at the MAC
(medium access control) layer. First, the node that
determines the link breakage, broadcasts the route error
packet containing the broken link information. Once
receiving a route error, a node updates its route cache so that
all source routes containing the broken link are shortened at
the point of failure.

If node receiving a RERR (Route Error) propagates), it
further only if there exists a cached route containing the
broken link and that route was used before in the packets
forwarded by the node. Note that using this scheme route
errors reach all the sources in a tree fashion starting from the
point of failure. In effect, route error information is
efficiently disseminated to all the nodes that forwarded
packets along the broken route and to the neighbors of such
nodes that may have acquired the broken route through
snooping.

B. Clock based Expiration of Route - If we recall that link
breakage is detected only by a link layer feedback, when an
attempted data transmission fails. Thus loss of a route will
go undetected if there is no attempt to use this route. A more
proactive clock-based approach will be able to fresh up such
routes. A clock based approach is based on the hypothesis
that routes are only valid for a specific amount of time
(timeout period) from their last use. Each node in a cached
route now has an associated timestamp of last use. This
timestamp is updated each time the cached route or part
thereof is "seen" in a unicast packet being forwarded by the

node. The main portions of cached routes unused in the past
interval are pruned. The advantage of this approach depends
critically on the proper selection of the timeout period A
very small value for the timeout may cause many
unnecessary route invalidations, while a very large value
may defeat the purpose of this technique. Although well-

chosen static values can be obtained for a given network, a
single timeout for all the nodes may not be appropriate in all
scenarios and for all network sizes. Therefore, a dynamic
mechanism is desirable that allows each node to choose
timeout values independently based on its observed route
stability. The proposed technique heuristic approach for
adaptive selection of timeouts locally at each node based on
the average route lifetime and the time between links breaks
seen by the node. When a cached route breaks due to link
breakage or upon receipt of a route error, the lifetime of the
broken route is computed as the time elapsed since it was
last entered in the cache. Average route lifetime is obtained
using the lifetimes of all broken routes in the past. Time of
latest link breakage seen by a node is also maintained.

When route breaks occur uniformly in time, average route
lifetime itself provides a good estimate. However when
many route breaks occur in short bursts with a large
separation in time, the average route lifetime does not
accurately predict during the periods of no route breaks. The
value of route life time is computed periodically and is used
to expire stale entries from the cache. In the experiments,
every half a second and route cache is computed then
checked for stale entries.

C. Unconstructive Caches - In order to improve error
handling in DSR, caching of negative information has
already been recommended. In order to make use of this
way, every node caches the broken links seen recently via
the link layer feedback or route error packets. Within a
interval of creating this entry if a node is to forward a
packet with a source route containing the broken link, (i) the
packet is fallen and (ii) a route error packet is produced. In
addition, the negative cache is always checked for broken
links before adding a new entry in the route cache.
Essentially, route cache and negative cache are mutually
exclusive with respect to the links present in them. This
prevents the cache pollution problem.

D. Energy Consumption Model

All the discussions in this section and the following
sections correspond to a mobile graph U M - U 1 U 2 ...U T
generated for an source-destination (s-d) session by
sampling the network topology at instants of packet
origination ts^ ts 2 , . . ., ts T . Let Pk = v Vi- ■ .vp be the static s-d
path in U, = (R„ O,) at time tsi. Here, v = source and V; =
destination and (vp.j, vp) ^ O, forp = 1,2, ..., / are the hops
of the s-d path. All the energy consumption calculations for
the s-d path at time tsi are strictly based on the snapshot of
the network topology Uz at tsi. Thus the queuing delays and
propagation delays are neglected while assuming infinite
channels. The packets are being instantaneously transmitted
from source s to destination d.

The energy consumed for a node to node traffic on the
source-destination path Pi is modelled as the sum of the
energy consumed along each hop. The energy consumption

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

is modelled per hop considering complete overhearing (non-
destination nodes receive the entire data packet), a reduced
overhearing case where the non-destination nodes discard
the data packet after scanning its header and when there is
no overhearing.

The over hearing costs are presented at the non-destination
nodes for each of the three following cases:

Ptop_Tx(vj-i) =

Sizeof ReqToSend + Sizeoforiqinaldata + PDR^

Transenergy( ■ ! ■ — )

Bandwidth

ii. Decreased overhearing

Instead of the receiving the entire data packet, a node
could scan only the header of the data packet and discard the
remaining of the packet. Thus, the non-destination nodes at
the neighbourhood of the sender v pA are excited for only
receiving the data packet header and the Request To Send
packet. On the converse, the non-destination nodes at the
neighborhood of the receiver vj are charged for receiving the
ACK and CTS packets. This simple strategy can bring
significant power savings when the data size is considerably
larger than the size of the header preceding the actual data in
the data packet.

. . SizeofClea rtosend + Acksize + RERR .

Re ceenergy * ( ■ )

Bandwidth

(1)

Ptop_Rec(vj-l) =

SizeofClearToSend + ACKsize + RERR .

Transenergyi ■ )

Bandwidth

. , Sizeof Re qtosend + Datasize + PDR s
Re ceenergy * ( ■ ! : )

Bandwidth

(2)

Absolute overhearing

Here the nodes are operating in promiscuous mode. The
non-destination nodes at the neighborhood of the sender v p _\
are stimulated for receiving the entire data packet. Including
these nodes are charged for receiving the Request To Send
(RTS) packet. Likewise, the non-destination nodes at the
neighborhood of the receiver v p are charged for receiving the
Acknowledge (ACK) and Clear To Send (CTS) packets.

V / e Nei_Nodes(v p . lt t si ), AOvhe(l, v p . h t si )

_ . , Sizeof Re qtosend + Datasize + PDR ,
Re ceenergy * (

Bandwidth

(3)

V / e Nei_Nodes(Vp_i, t si ), AOvhe(l, v p _ h t si )

Re ceenergy *
PSizeof Re qtosend + Data _ Neigh _ size + PDrop

Bandwidth

V /i£ Nei_Nodes(v p , t si ), AOvhe(h, v p .j v p , t si )

(5)

iii. Absence of Overhearing

If node enters the snooze or sleep state when there is an
ongoing transmission in its neighborhood in which the node
is neither a transmitter nor a receiver. If an intended receiver
of the data packet is assumed to be notified by the sender
through energy-efficient IEEE 802.11 ATIM frame
mechanism [11]. Nodes are assumed to be identified their
neighbors through the beacon frames exchanged as part of
the power saving mechanism. The energy consumed for the
transmission and reception of the ATIM and beacon frames
is assumed negligible. Such an assumption may not be
completely true because when the topology changes more
frequently, power saving strategies require nodes to be
awake at least half of the beacon interval. On the other hand,
the maximum energy savings are evaluated that could be
obtained when the cost of overhearing is totally discarded.

V / 6 Nei_Nodes(v p .!, t si ), AOvhe(l, v p . 1: t si ) = (6)

V h e Nei_Nodes(v p , t si ), AOvhe(h, v p .,v P) t si ) = (7)

iv. Stale Route Avoidance in DSR

Re ceenergy * (

(4)

SizeofClearToSend + ACKsize
Bandwidth

RERR Nodes movements result stale route cache entries.

) Cache staleness is a big problem in link cache scheme
where individual links are combined to find out best path
between source and destination. A cache timeout policy is
required to expire a route cache entry, when it is likely to
become stale. DSR makes aggressive use of route cache to
avoid route discovery. The performance of DSR heavily
depends on efficient implementation of route cache. In this,
a new cross-layer approach for predicting the route cache

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

lifetime is presented. This approach assigns timeouts of
individual links in route cache by utilizing Received Signal
Strength Indicator (RSSI) values received from wireless
network interface card.

/* Source Initiated Energy Efficient Algorithm*/

{

if (LB =1) RERR is transmitted and Route cache is updated

}

if (Tout = 1 ) Valid routes are determined

else if ( Negative caches )

{

Cache Pollutions are determined & cache freshness are initiated.
If( CO = 1 )

{

Nodes operating in promiscuous mode.

}
else(RO= 1)

{

Node scan only the header of the data

}

if( NO =1) Maximum Energy Savings are determined.

}

{

Total Energy Consumption = E C o+E N o + E RO

else route cache is updated

}

IV. PERFORMANCE ANALYSIS

We use NS2 to simulate our proposed algorithm. In our
simulation, 101 mobile nodes move in a 1000 meter x 1000
meter square region for 50 seconds simulation time. All
nodes have the same transmission range of 100 meters. The
simulated traffic is Constant Bit Rate (CBR). Our simulation
settings and parameters are summarized in table 1

TABLE I. SIMULATION SETTINGS AND PARAMETERS

No. of Nodes

101

Area Size

1000X1000

Mac

802.11

Radio Range

100m

Simulation Time

50 sec

Traffic Source

CBR

Packet Size

80 bytes

Mobility Model

Random Way Point

A. Performance Metrics

We evaluate mainly the performance according to the
following metrics.

Control overhead: The control overhead is defined as
the total number of routing control packets normalized by
the total number of received data packets.

End-to-end delay: The end-to-end-delay is averaged
over all surviving data packets from the sources to the
destinations.

Packet Delivery Ratio: It is the ratio of the number .of
packets received successfully and the total number of
packets transmitted.

The simulation results are presented in the next part. We
compare our proposed algorithm with DBEE - CLA [18]
and RANDOMCAST in presence of overhearing
environment.

Figure 3 shows the results of average end-to-end delay
for varying the nodes from 20 to 100. From the results, we
can see that SIEE scheme has slightly lower delay than the
RANDOMCAST and DBEE-CLA scheme because of
authentication routines.

NODES Ys DELAY

4.5

I '
S 2.5

a 1-5
i

as
z-

-RANDOMCAST

-DBEE-CLA

-KIHH

Nodes

Fig. 3. Nodes Vs End to end Delay

Fig. 4, presents the energy consumption. The
Comparison of energy consumption for SIEE, DBEE-CLA,
RandomCast. It is clearly seen that energy consumed by
SIEE is less compared to RandomCast and DBEE-CLA.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Node Velocity Vs Total Energy Consumption

I RANDOMCAST
I OBLt-t'LA
SIEE

10 70 aa 10 SO

ft [ ;s\iruum Norte Velocity (ni/s)

Fig. 4. No. of Nodes Vs Energy Consumption

Fig. 5, presents the comparison of overhead. It is clearly
shown that the overhead of SIEE has low overhead than the
RandomCast and DBEE-CLA.

Throughput Vs Overhead

MUM)

_5000 -

^"^*

% JOtWl

^*^e*^r _+~-*~~^ — ^UHht ctA

° 100(1

^ * — ^SIEE

S 10 IS 20 IS

Throughput

Fig. 5. Throughput Vs Overhead

Figure 6 shows the results of Mobility Vs Delay. From the
results, we can see that SIEE scheme has slightly lower
delay than the RANDOMCAST and DBEE-CLA scheme
because of authentication routines.

Mobility Vs

Delay

0.8
0.6 ■
0.4 -

ttt

■ KrtNIMJMGAM

0.2 ■

K-tl

"t-

■ DBFF a A

15 20

■ Sltt

Mobilil.vfs)

Mobility Vs Total energy consumption

•§ 1400 -

,_ __ ~ ■

g 120(1 J

^B — — ~ — — — _

g ^ 1000 ■*-'■

§ 3 soo -"'■" ~fc— -

'"' ^ 600 f''^fc~~^fc—

fc— ~L _ ■ RANDOMCAST

?* ^ 400 -f '■ f V 1—

I LlUtt-tlA

200 ■ Hh
1 10 m

■ SIEE

30 ^TrT 7

Mobillty<s)

Fig. 7. Mobility Vs Overhead

Fig. 7, presents the comparison of total energy
consumption while varying the mobility from 10 to 50. It is
clearly shown that the energy consumption of SIEE has low
overhead than the RandomCast and DBEE-CLA.

Mobility Vs Delivery Ratio

LA
1.2

P o.a
g

"3 [j.(.

a. a

0.2

:■

-RANDOMCAM

-DBEE-CLA

-Sltt

30 40

Mnhilltv

Fig. 6. Mobility Vs Delay

Fig. 8. Mobility Vs Packet Delivery Ratio

Figure 8 show the results of average packet delivery ratio
for the mobility 10, 20... 50 for the 100 nodes scenario.
Clearly our SIEE scheme achieves more delivery ratio than
the Randomcast and DBEE-CLA scheme since it has both
reliability and security features.

V. Conclusion

In MANET, mobile nodes are moving randomly
without any centralized administration. Due to that, the node
consumes more energy unnecessarily. In this paper, we have
developed a source initiated energy efficient algorithm with
energy consumption model which attains minimum energy
consumption to the mobile nodes. In the first phase of the
scheme, route cache update and stale route avoidance is
achieved using SIEE algorithm. In second phase, minimum
energy consumption is achieved using energy consumption
model. It uses three factors called utility factor, energy
factor, mobility factor to favor packet forwarding by
maintaining minimum energy consumption for each node.
We have demonstrated the energy estimation of each node.
By simulation results we have shown that the SIEE achieves
good packet delivery ratio while attaining low delay,

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

overhead, minimum energy consumption than the existing
schemes Randomcast and DBEE-CLA while varying the
number of nodes, node velocity and mobility.

REFERENCES

[1]. Perkins C. Ad Hoc Networking: Addison-Wesley:
2001; 1-28.

[2]. David B. Johnson, David A. Maltz and Yih-Chun
Hu, "The Dynamic Source Routing Protocol for
Mobile Ad Hoc Networks (DSR)," Internet Draft,
draft- ietf-manet-dsr-09.txt, 15April2004.

[3]. Fang Liu, Kai Xing, Xiuzhen Cheng, Shmuel
Rotenstreich , "Energy-efficient MAC layer
protocols in ad hoc networks" Resource
Management in Wireless Networking, Kluwer
Academic Publishers, 2004, pp. 1-42.

[4]. Lim S, Yu C and Das C, "Rcast: A Randomized
Communication Scheme for Improving Energy Efficiency
in Mobile Ad Hoc Networks," Proc. 25th IEEE Int'l Conf.
Distributed Computing Systems, pp. 123-132, 2005.

[5]. Ashish K. Shukla and Neeraj Tyagi, "A New Route
Maintenance in Dynamic Source Routing
Protocol," International Symposium on Wireless
Pervasive Computing, Phuket, 2006.

[6]. Jung E and Vaidya N, "A Power Control MAC
Protocol for Ad Hoc Networks," Proc. ACM
MobiCom, pp. 36-47, 2002.

[7]. Kyasanur P, Choudhury R. and Gupta I, "Smart
Gossip: An Adaptive Gossip-Based Broadcasting
Service for Sensor Networks," Proc. Second IEEE
Int'l Conf. Mobile Ad Hoc and Sensor Systems,
pp. 91-100, 2006.

[8]. Laura Marie Feeney, "An Energy Consumption
Model for Performance Analysis of Routing
Protocols for Mobile Ad Hoc Networks", Mobile
Networks and Applications, 2001, pp.239-249.

[9]. V.Ramesh et.al, "An Efficient Energy
Management Scheme For Mobile Ad-hoc
Networks", International Journal of Research and
Reviews in Computer Science (IJRRCS) Vol. 1, No. 4,
December 2010, pp. 173- 176.

[10]. Ashish K. Shukla and Neeraj Tyagi, "A New Route
Maintenance in Dynamic Source Routing Protocol,"
International Symposium on Wireless Pervasive
Computing, Phuket, 2006.

[1 1]. Charles E. Perkins and Elizabeth M. Royer, 'Ad-Hoc On-
Demand Distance Vector Routing," In Second IEEE
Workshop on Mobile Computing Systems and
Applications, pages 90-100, February 1999.

[12]. Ashish Shukla, "Ensuring Cache Freshness in On-
demand Routing Protocols for Mobile Ad Hoc Network:
A Cross-layer Framework " IEEE CCNC, 2007.

[13].Sangeetha et.al, "Energy Efficient Routing In
MANET Using OLSR", International Journal on
Computer Science and Engineering (IJCSE), Vol.
3 No. 4 Apr 201 1, pp.1418-1421.

[14]. B. H. Liu, Y. Gao, C. T. Chou and S. Jha, "An
Energy Efficient Select Optimal Neighbor

Protocol for Wireless Ad Hoc Networks,"
Technical Report, UNSW-CSE-TR-0431,
Network Research Laboratory, University of New
South Wales, Sydney, Australia, October 2004.

[15].L.M. Freeny, — Energy efficient communication
in ad hoc networks,! Mobile Ad Hoc Networking,
Wiley-IEEE press, pp. 301-328, 2004.

[16]. S.Singh and C.S.Raghavendra, IIPAMAS power
aware multi-access protocol with signaling for ad
hoc networks,! ACM Computer Communication
Review, vol. 28(3), pp.5-26, July 1998.

[17].Sofiane Boukli Hacene and Ahmed Lehireche,
"Coherent Route Cache In Dynamic Source
Routing For Ad Hoc Networks", Computer
Science Journal of Moldova, vol.19, no. 3(57),
2011,pp.304-319.

[18].R.Bhuvaneswari & Dr.M.Viswanathan, " Demand
Based Effecive Energy Utilization in Mobile Ad
Hoc Networks", International Journal of Computer
Science Issues, Vol.9, Issue2, No. 2, March 2012,
pp.439-445.

AUTHORS PROFILE

M.Viswanathan. Senior Deputy Director in
Fluid Control Research Institute (FCRI), a
Public Sector Undertaking under Government
of India, Palakkad, Kerala, India. He obtained
graduate degree in Electronics and
Communication Engineering from the
University of Madras, Madras, India, received
his Master's Degree in Electronics Engineering
from IIT Kanpur and Doctoral Degree in
Electronics from Bharathiar University, Coimbatore, India. He has 25
years of vast experience and had undergone training at NEL, UK in the
area of flow measurement and B&K, Denmark in the area of Noise and
Vibration. He has published many Technical papers in International
Journals and presented papers in conferences held in India and abroad.

R.Bhuvaneswari has received the BE. Degree in
Electronics and Communication Engineering
from Govt. College of Technology, Bharathiar
University, Coimbatore, Tamil Nadu, India in
1989, M.E.Degree in Applied Electronics from
the Govt. College of Technology, Bharathiar
University, Coimbatore, Tamil Nadu, India in
1994 and she is currently pursuing Ph.D. degree
in Electronics and Communication Engineering
from the Anna University of Technology Coimbatore Coimbatore,
Tamil Nadu, India. Her areas of interests include Communication
networks, Wireless Communication, Mobile Communication, Digital
Communication, Biomedical Applications, Applied Electronics,
Computer Networks, Mobile Adhoc Networks (WiFi, WiMax,
HighSlot GSM) & Network Security. She has also presented recently
two papers on Mobile Adhoc Networks in conferences held in India.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

ASSESMENT OF COBIT MATURITY LEVEL

WITH EXISTING CONDITIONS FROM AUDITOR

I Made Sukarsa \ Maria Yulita Putu Dita 2 , 1 Ketut Adi Purnawan 3

' ' Faculty of Engineering, Information Technology Studies Program, Udayana University
Kampus Bukit Jimbaran, Bali, Indonesia

1 e_arsa@yahoo . com, 2 dita_pink4ngel@yahoo . com, 3 dosenadi@yahoo . com

Abstract — COBIT is a method that provides a basic framework
in creating an information technology appropriate to the needs of
the organization by taking considering other factors that affect.
COBIT can be used as a guide to conduct an audit of the
feasibility of an investment in information technology that has
been done by a company. COBIT has a measuring element of IT
performance, a list of critical success factors and maturity level
measurement. All these tools are designed to support the
successful implementation of corporate governance on various
objects in the field of IT. These research take a case studied at
employment agencies which usually called BKD. Domain selected
in this study is Delivery and Support (DS). The measurement
level of maturity is seen that the whole process of IT is at a scale
of 3, which means the level of maturity in BKD is defined. This
shows that none of current IT process has the same level with the
expected one. The whole process still has a gap to be closed.
Appropriate IT processes are given the initial step for the
development of IT models include DS1, DS5, DS7, DS10 and
DS11 with the determination of CSF, KPI and KGI. Substantive
tests performed in this study meet the sufficiency of the evidence
in order to obtain a conviction of compliance with the conditions
of the criteria. BKD substantive test results are already has the
documentation to third parties (vendors) and documentation are
clearly procedures for managing civil servants information
system which usually called SIMPEG. But there are some things
you may need to be added to the application, such as the lack of
backup, recovery, an automated help menu in SIMPEG and
password to be encrypted. Substantive test also intended to
develop opinions, conclusions and recommendations for the
management of BKD by improving the management of
information systems in the future.

Keywords-COBIT; Maturity Level; Substantive Test;
Recommendation; IT Models

I. Introduction

BKD as a government agencies that deal with personnel
issues can not be separated from the existence of aspects of
centralized data management. Data management at BKD, as a
process that is beginning to implementation management
information systems to strengthen personnel administration in
an effort to meet employee needs for information of accurate,
accountable, and up to date data. Manual conversion data into
digital data into a computerized database and applications in a
container called a SIMPEG, is expected to increase the

effectiveness and efficiency in the processing, storage,
presentation and recap information related to staffing. If the
system is not computerized information used properly it could
lead to information needs to be blocked and will interfere with
the performance of the relevant agencies. Utilization of IT to
support the achievement of organizational goals and
objectives must be balanced with effectiveness and efficiency
of management. Therefore, IT audit should be done to
maintain the security of information systems as an
organizational asset, to maintain the integrity of information
stored and processed and of course to increase the
effectiveness of the use of IT and support efficiency in BKD.
(BKD, 2011).

A good management for information systems can supported
the company's performance. IT governance is managed
properly can be used as landing in a large organization or a
government. COBIT (Control Objectives for Information and
related Technology) is one standard for IT audit can be used
to measure. COBIT focuses on controls and provides a set of
best practices for management. Such as ensuring the delivery
of services and can provide the measurement and rate when
errors occur. (COBIT 4.1, 2007)

In this study, COBIT is also used as tools for effective
implementation of information systems within the company.
Measurements were performed to determine the condition of
the current IT governance in BKD are expected to set targets
based on factors that influence, with the fundamental to the
COBIT maturity model framework, so that the gap obtained
from the maturity level. Then to determine whether a problem
or irregularity actually occurred or not the substantive testing
can enhance the acquisition of evidence in gaining confidence
in the company's compliance with the criteria condition. Tests
conducted on a review of several documents and procedures
related to IT management and the testing of applications
SIMPEG.

II. Theoretical Framework

A. IT Governance

As mentioned previously, the application of good corporate
governance in the company relies heavily on IT governance is
carried out in earnest starting from top level management to
the staff. IT governance plays a role in measuring the

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

company's business processes to ensure its effectiveness in
supporting the goals and objectives of a company. The
following is a description of the five sections that are focused
in IT governance. (Gondodiyoto, 2007). These images below
are descriptions of five sections that focused in IT governance.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
strategic plan, and determine the information architecture.
(Gondodiyoto, Sanyoto, 2007).

Figure 1 . IT Governance Focus Area
(COBIT 4.1, 2007. Page 6)

Explanation for the main focus areas of IT governance are

as follows (COBIT 4.1, 2007):

a. Strategic Alignment is about focusing Strategic assurances
as to the relationship between business and IT strategy and
alignment between IT and business operations.
Value Delivery is included matters related to the delivery
of value that focuses on optimizing costs and proving the
existence of intrinsic value of IT.

Risk Management is about the application of IT to be
accompanied by the identification of IT risks that impact
can be properly resolved.

d. Resource Management is concerned about optimizing
critical IT resources, including: applications, information,
infrastructure and human resources. This area is key to
optimizing knowledge and infrastructure.

e. Performance Measurement is about tracking and
monitoring the implementation of the strategy, which runs
the project fulfillment, resource usage, process
performance and delivery by using a framework such as
the balanced scorecard.

B. COBIT definition

COBIT (Control Objectives for Information and Related
Technology) is a referral guide for IT governance to address
the gaps that are owned by the business risks, control needs
and technical issues. IT resources are highlighted COBIT,
including the fulfillment of business requirements for
effectiveness, efficiency, confidentiality integrity, availability,
reliability compliance, and information. COBIT can provide a
signal at danger or risk would appear to provide readiness to
deal with it. COBIT is useful for the auditor as a technique
that can assist in the identification of IT control issues.

COBIT can be used as a standard for the auditors, the
management of an organization or user. COBIT users can
obtain the advantage of confidence in the reliability of the
applications used. While managers to profit-making
investments in IT and infrastructure, developing the IT

C. COBIT Framework

COBIT Framework consists of three main parts, namely IT
Process, IT Resources and Information criteria. In the picture
below, indicated that the Information criteria are divided into
seven information criteria are then grouped into three aspects,
namely the quality requirements, fiduciary requirements and
security requirements. COBIT IT resources highlights the five
main sections, namely people, application systems,
technology, facilities and data. For IT Process consists of
domain, process and activities. (COBIT 3rd Framework,
2000).

Information Criteria

Figure 2. COBIT cube
(COBIT 3 rd Framework, 2000. Page 16).

D. COBIT Maturity Model

COBIT also provides IT process maturity level models to
evaluate the maturity level of the organization to have.
Assessment methods have COBIT is from a non-existent scale
of to 5-optimized. COBIT Maturity Model can be identified
as follows:

• Where are the company's performance

• Comparison of current industry status

• Target companies for repairs

• Track the growth needed between 'as is' and 'to-be'

To make the results easily usable in management briefings,
where they will be presented as a means to support the
business case for future plans, a graphical presentation method
needs to be provided (Figure 3).

Initial/ Repeatable
Non-existent Ad Hoc but Intuitive

Defined Managed and

Process Measurable Qptimised

LEGEND FOR SYMBOLS USED

Enterprise current status
^^- Industry average
Enterprise target

LEGEND FOR RANKINGS USED
— Management processes are not applied al all.
1 — Processes are .itllmcawi disorganised.
2 — Processes follow a regular pattern.
3 — processes are documented and communicated.
4 — Processes are monitored and measured.
5 — Good practices are followed and automated.

Figure 3. Maturity Model COBIT
(COBIT 4.1, 2007. Page 18)

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

The advantage of using COBIT maturity model is the
management can easily know the condition of the company. 0-
5 scale based on a simple maturity scale showing how the
process evolved from the ability of a lack of optimized.
(COBIT 4.1, 2007; 18.).

E. Critical Success Factor (CSF)

Critical Success Factors is the most important thing that
must exist within an organization or company because it can
help the company achieve its goals through IT owned. Critical
Success Factors in addition to providing a guideline for
implementing management control over IT and its processes
can also give a strategy, technical, organizational processes or
procedural nature. Areas highlighted here is related to the
ability and skills, focused and action oriented, and resource
utilization (COBIT Management Guidelines, 2000).

F.KPIandKGI

Key Performance Indicator (KPI) and Key Goal Indicator is
an indicator that measures are also provided for each of the
COBIT IT processes. KPI are usually indicators of capabilities,
implementation, and the ability of IT resources. KPI focuses
on how the process is run, while KGI focuses on the process.
Based on the principles of Balanced Score Card, then the
relationship between the Key Performance Indicator targets
and indicators are as follows (COBIT Management Guidelines,
2000) :

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
most important goal in performing substantive tests is to meet
the substantive adequacy of the evidence by applying a broad
and appropriate types of testing, as well as to enhance the
acquisition of evidence to obtain reasonable assurance about
compliance with the conditions of the field previously
obtained criteria. Then, the results of the testing phase of the
findings. The audit findings will stem the decline in the
comparison condition (what is actually happening) with the
criteria (what should happen), reveals the impact of
differences in the conditions and criteria and to find the cause.
(Nurharyanto, Ak., 2009).

Goals <

-J

jiW

Enablers

lit

III. Methodology

Information systems audit refers to the standard COBIT
framework. Related to vision, mission, goals and targets in
BKD mostly lead to the delivery and support services
provided by a system of information technology (IT) that has
been applied, the study also focuses on the domain Deliver
and Support (DS), especially the DS1 up with the DS13. In
the implementation of this study, where the case studies is the
BKD.

This study describes how the application of IT Governance
is happening at BKD. The research method used in this study
is a qualitative method using case studies for helped the
authors to obtain an understanding of an event. Studied as a
case study of an integrated whole, where the goal is to develop
a deep knowledge of the object in question, which means that
case studies should be characterized as an exploratory and
descriptive research. The steps undertaken in this study
include the stages of domain selection, data collection, data
processing and determination of recommendations, as seen in
Figure 5 below, which is the sequence of research steps.

KGI
(measure of outcome)

KPI
(measure of performance)

Figure 4. KPI and KGI relationship
(COBIT Management Guidelines, 2000. Page 20)

Key Performance Indicator and Key Goal Indicator have
linkages and causal relationships in support of the IT
processes, which shows how well the process can allow the
objective can be achieved. Meanwhile, key goal indicators
focusing on "what", while the Key Performance Indicator
focused on "how". Usually, Key Objectives and Key
Indicators Performance Indicators will often be the size of the
Critical Success Factors (CSF) and when it is monitored and
acted upon, it will identify opportunities for process
improvement. These improvements have a positive influence
on results. (COBIT Management Guidelines, 2000. Page 20).

G. Substantive Test

Substantive tests performed by the auditor as a follow-up
audit to obtain reasonable assurance about whether the
findings of the auditor while, prepare opinions and
conclusions, and develop suggestions for improvement. The

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

Determining the Problem formulation, Objectives & Limitations

Study Literature

Hussiness Goal
Identification

IT Goal Identification

Domain Selection Process

IT Process Identification

Mapping to COBI I
framework

Control Objectives
Identification

Data Collection

Survey for Maturity Level & Interest
Rate Questionnaire

Current & Expected IT
Maturity Level
Measurement

Gap Analysis

Detailed Control
Objectives

Substantive Test

Data Processing & Analysis

I ,

Interest Rate
Measurement

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
also conducted to determine the process, the stages are done
now associated with information technology resource
management, decision-making processes, information
technology investment management processes and ideal
expectations based on management's view of the company.

The design of this questionnaire refers to the COBIT
Implementation Tool Set and questionnaire aims to obtain
data or any official opinion of BKD as a party related to IT
management. Questionnaire to determine the interest rate is
developed to determine the importance of each IT process in a
DS domain. Column containing the level of importance,
respondents could choose one answer that is considered to
represent actual field conditions by providing a tick ( V ) in the
space provided. Later the results of this questionnaire will be
calculated the score of each IT process is considered to have a
high contribution to the business goals or have a high interest
rate to be selected in the provision of recommendations to
improve IT governance process. The following is a
questionnaire designed for the interests of the DS domain of
IT.

CSF

KGI

KPI

Recommendations for improvement
based on findings

Determining the
Recommendation

Preparation of Report

Figure 5. The Steps of Research

To conduct survey on IT governance, especially in the
process of managing data is by using questionnaires and
interviews.

The questionnaire is a collection of data by using a list of
statements that are used to determine respondent's perceptions
of several variables considered in the implementation of IT
governance and management of information systems within
the company. Data collected is taken directly from the data
obtained from the respondents that the questionnaire
addressed to the head section, sub. section and the staff
members at BKD with a view to obtaining the target
achievement and the assessment of the achievements that have
been implemented.

This study uses two types of questionnaires, ie
questionnaires to measure the maturity level and the
importance of IT in DS domain using COBIT standards.

Interview process conducted to determine the existing
business processes in the company. The first stage in the
interview process is to identify the parties responsible for each
process that takes place in the company. Interview technique
is also a collection of data by direct questioning by the
respondent in order to obtain information from the
questionnaires have not been accommodated. Interviews were

Importance

i
1

IT PROCESS
EhetxiMer and Support (DS) Domain

DS 1

Define and Manage Service Levels

DS 2

Manage T'\i- i d-p:p-\- Ssj-vfces

DS 3

Men-age Perfomixi~.ce and Capxc.u

DS 4

Ensure Ccn-.ina.is Zei-w'ces

DS 5

Ensure System Security

DS 6

I;"dt>)~~;fj ar-o. Allocate. Cast

DS 7

Educate and Train Users

DS S

Manage Service desk and incidents

DS 9

Manage z~>\<? Cznfl^iirciiioYss

DS 10

.'. ianage Problems

DS 11

Manage Data

DS 12

Manage the Physical Environment

DS 13

Manage Operations

Figure 6. Importance Questionnaire

Examples of the draft questionnaire was also made in this
study, the questionnaire for the measurement of the level of
decency on the maturity level to level the IT Process DS 1 .

a D^l Define and Miinnge Service Levels

Do You Aeree ?

I'rfiiir ni'.IM:i:i:f vi-vKrlivel;

Maturity Level

Management oas not recognised the need for a process for
iti'L^ics -LciTice lewis

Ac-:cT»:\ii:.J:t:e\. ni"...li-e ; ,p:i".:iV.:l:t^':. f;-i
■::.:■:-■.::.:.; fhem ate not .-..■.; ;.::- '

Gjiapliaiice Level = 9.00

Figure 7. Maturity Levels Questionnaire

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

In the design of the questionnaire above shows there are
several components in the checklist. Component indicated by
the number 1 is the name and number of IT processes are
observed. Component indicated by the number 2 is the level
of maturity that will be used to distinguish the contribution of
each level. Component 3 contains a description of the
statement used to guide the interview questions. Component 4
is a scoring guide as a number of observations and interviews
of each statement is expressed. Component 5 is the sum of the
value of each statement, which will be used as the
contribution of each level and component 6 is the total weight
of the total number of questions. Any item that is the question
on the DS1 with a maturity level as shown above is referring
to the standard IT Governance Institute Team pages 104 in
thebookofCOBIT4.1.

IV. Results And discussion

A. Determination Process Domain

Based on the mapping and identification of objectives and
goals BKD with standard business goals and IT goals COBIT,
the importance of the domain of IT processes that are relevant
to the audit, namely:

Table 1. IT Determination Process in accordance with enterprise IT Goals

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
existing questionnaire is based on the following values
(Pederiva, 2003)

IT Process

IT Domains

POl, P02, P03, P04, P05,
P06, P07, P08, P09, PO10

Plan and Organize

All, AI2, AI3, AI4, AI5,
AI6,AI7

Acquire and Implement

DS1, DS2, DS3, DS4, DS5,
DS6, DS7, DS8, DS9, DS10,
DS11,DS12,DS13

Deliver and Support

ME1,ME2,ME3, ME4

Monitoring and Evaluate

However, due to BKD is a government agency that
specializes in managing all the data of personnel has the
vision and mission and goals and objectives are more directed
at the delivery of IT services for civil and environmental
SKPD in Bali Province, such as increasing the
professionalism of staff with training, setting security and data
management including operational facilities, the study of this
audit focuses only on the 13 IT processes in the Deliver and
Support domain.

B. Measurement Maturity Level

Capability and maturity of each IT process in Deliver and
Support domain will then be identified. Implementation level
of maturity for BKD questionnaire will reveal the condition of
maturity of each process at this time. Assessment of each IT
process maturity level refers to the COBIT maturity model
Guidelines Management. Value will indicate the level of
maturity of IT process maturity level with a thorough
identification of each level. Weighting is performed on an

Table 2. Questionnaires weighting

Answer

Value

Not at all

A little

0,33

Quite a lot

0,66

Completely

Then the mapping of the entire questionnaire with a weight
value of the statement above will be summed and divided by
the amount of the existing statement. Values obtained from
the division is then a standard level of maturity in accordance
with the table below. (Djatmiko, 2007)

Table 3.

Assessment Criteria

Maturity Index

Maturity Level

- 0,50

- Non-Existent

0,51-1,50

1 - Initial/ad hoc

1,51-2,50

2 - Repeatable But Intuitive

2,51-3,50

3 - Defined Process

3,51-4,50

4 - Managed and Measurable

4,51-5,00

5 - Optimised

Level is determined based on the appropriateness of the
COBIT framework provides grouping capability in managing
the company's IT processes from level zero to level five
(optimized). The thing to note is that the level is not intended
to be a sequential increase that must be met starting from the
lowest to highest. Fulfillment can do some level of decency
for simultaneously. The point is the fulfillment of maturity
may occur at some level zero to level five, then the level of
compliance maturity calculated in accordance with the total of
the multiplicative contribution to the level of appropriateness
of the levels concerned. Thus, the determination of the same
will be done at each level in the IT -related processes.

The following measurements will show the maturity level
of an employee at BKD are processed using Microsoft Excel
by taking a sample calculation for the DS 1 from a respondent
and starting from level to level 5.

.;■-.

Criteria Value

:.-

Level

Contribution
Level

Value

]

:.:":

1,30

1,66

3,33

0,0

11,00

:.::

:::

3,50

0,3

::■

:::

1,30

:::

16=

y«

3,40

0,7

11,28

:.::

ii.es

0,66

■i

D,«

3,*

3,66

1,0

0,66

:.:":

i,«

-:; -:;

::::

:;;

:.::

:,5S

ifi

3,53

0,76

:.::

3,66 0,66

tft

3,96

3,66

1,12

'..".-

tv level:

3.CC

Figure 8. Examples of Maturity Level Measurement

Seen in the table above which is a process that occurs in the
calculation to obtain the value of each level in the domain of
DS 1 . One example at level 1 , the weight of a column stating
that there are large number of statements in each level, and
each statement has a weight of 1, then at level 1, weighted
equally, ie, weight = 1, resulting in a total weight of 4. The

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

result is the selection by choosing one of the following criteria:
"Not at all", "A little", "Quite a Lot" or "Completely". Each
criterion has a certain value which is then assessed based on
the compliance level quotient between the sum value of each
criterion statement with a total weight of the previously
presented. Propriety maximum level is 1, which describes the
related statements have been fully met. In the table shows that
the level of appropriateness of IT processes to level 1 is at 0,5
which is the quotient between the total value of the criteria of
1,98 with a total weight of 4. Furthermore, the contribution of
the process give you an idea how much influence the
appropriateness of each IT process maturity level as a whole.
Contribution is then multiplied by the level of appropriateness
at each level of maturity. The results of time will be written in
the column value, total of five levels later in the DS1 will be
added together to determine the level of IT process maturity of
one respondent.

Maturity target of IT processes are ideal conditions for the
expected level of maturity, which will become a reference in a
model of good IT governance. Maturity target of IT processes
is determined by looking at the internal environment of
business and the high expectations of the management board
at BKD about COBIT IT processes to be applied. The vision
and mission, goals, and objectives of IT adoption in BKD can
be found several important issues that can be taken as a basis
for consideration to determine the expected target of process
maturity, which is evident in the goals and objectives of BKD
itself. Based on consideration of several factors and the high
expectations of the management ranks of the COBIT IT
processes in DS domain, it can be concluded that the level of
maturity that will become a reference in the IT governance
model to be developed is on a scale of 5 which has been
managed by optimal (optimized).

C. Gap Analysis Maturity of IT Processes

The table below shows the gap analysis of current business
conditions (current maturity level) and expected (expected
maturity level).

IT Process

DS 1 - Define and Manage Seixice Levels
DS2 - Manage TJtird-parlv Setrices

(iap

Maturity

lev!

Interest Rnte of IT Process in
BKD

liii|H.il:in!

>i»l ImjHjrtiint

2.3

81,58

IN 12

2,1

7k.uk

zix

DS3 - Manage Performance ami Capacity
DS-1 - Ensure Cant/nan* Setxices

2,2

63,64

16. 36

2.0

65.15

1 I.N5

DS5 - Ensure System Security

2,3

-,7.:i

2,75

DS6 - Indenttfy and Allocate Cost
DS7 - Educate and Train Users

2,0

SO. 76

19,23

1,8

"7 I 7

2,83

DSS - Manage Setxice desk and incidents

2,1

;n.ii>

M.'U

I )S'.> - Manage the ( 'onfigitratlons
DS 10 - Manage Problems

1 'i

;i 5s

[,K 12

I.S

K'J.OI

in.')')

DS11 - Manage Data

1.5

99.13

DS12 - Manage the Physical Environment

1.8

42,11

57.90

DS13 -Manage Operations

1.7

69,56

30,43

Figure 9. Gap for Maturity Level

-Current Maturity le^el

-I *|:„ :.), :: M.,I:miI V

Level

Figure 10 Graphic Display for Current and Expected Maturity Level

Based on the distribution of the maturity level (maturity
level) IT processes COBIT on DS domain is shown in the
graph above, it can be described as a condition in which all
the conditions in these domains are at maturity level 3. This
means that in general IT processes running on the BKD has
been defined in the standards or procedures are documented
and communicated through formal training and the
implementation still depends on the individual whether to
perform the procedure established or not. The procedure
created is still limited to the form of formalization of existing
practices. Ideal conditions are expected at maturity level 5
(optimized), a condition in which the process is carried out
has undergone continuous improvement process and thus
produce the best results. The use of integrated information
technology to automate organizational environment, readily
available tools and other support that can improve the quality
and effectiveness of performance, and the organization is
stable and able to adapt well.

D. Recommendation to Closed the Gap

Gap maturity level found on DS 1 to DS 1 3 process and can
be addressed by BKD to conduct activities or adjustment
measures as follows:

Table 4 Recommendations to Closed the Gap

Proses
TI

Recommendations to closed the gap

DS1

a. BKD need to implementation the procedures
to address the identified deficiencies of
formal service level.

b.Need agreement to the level of service that has
begun to lead to business needs.

DS2

a. BKD need for support measures for early
detection of potential problems with third-
party services.

b.Reviewing periodically at intervals specified
in the contract signed with third parties.

DS3

a. Need for improved handling performance and
capacity problems associated with such use of
monitoring tools can automatically detect and
fix problems related to performance and
capacity, so as to impact later on the staff and

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

users of IT services that are no longer doubt
the ability of IT services.

DS4

a. BKD need for an integrated IT service
processes in a sustainable manner with
respect to external benchmarking and best
practices.

b. Increasing the understanding of practice and
thoroughly enforced.

c. BKD need ongoing systematic measurements
for the goals and objectives towards the
achievement of IT services.

DS5

a. Running the IT security responsibilities have
been assigned consistently.

b. Reporting the need for security that includes a
clear business focus.

c. BKD need for security training and set up it
formally.

DS6

a. Monitoring and evaluation of the cost of
services used to optimize the cost of IT
resources.

b. Improving cost management to the level of
industry practice, which is based on the
results of continuous improvement and
comparison with other organizations.

DS7

a.The need for education and training process
monitoring and detection of irregularities
should be further improved.

b.Applied to the analysis of the need for IT
education and training issues.

DS8

a. Question need for tracking and automatic

incident reporting system and formal

implementation,
b. IT services need to train personnel, and the

process is enhanced through the use of special

software.
c.BKD need for comprehensive FAQs from a

part of the knowledge base.
d. Completion of the quick incidents consistent

advice in a structured escalation process.

DS9

a. BKD need consistent action for physical
verification and detection of irregularities
further enhanced procedures.

DS10

a. Detection of violations of norms or standards
that have been defined.

b. Individual asset tracking and monitoring are
used to protect IT assets and to prevent theft,
abuse and harassment.

DS11

a. Institutionalization and seriousness to handle
the training for data management staff
members.

b.The necessity of understanding for the needs
in data management and understanding of all
necessary actions within the organization.

c. Responsibility for data ownership and data
management are clear, widely known
throughout the organization and updated in a
timely manner.

DS12

DS13

a. BKD need for setting standards for all
facilities, including site selection,
construction, guarding, personnel safety,
mechanical and electrical systems, and
protection against environmental factors (eg,
fire, lighting, flood).

b.The need to document the security
requirements of the physical environment and
access is strictly controlled and monitored.

c.The need for more stringent measures and
continuous monitoring of access control and
all visitors are escorted at all times.

a. Maintenance and service agreements with
vendors that are formal.

b. Need for regular reporting on the events and
results to management tasks.

E. Measurement of Interest Rate

The collection of data on interest rates was conducted using
questionnaires that have interest rate indicator assessment of
the level of importance. Respondents were assigned to choose
the right answer according to the condition of the company.
Made to the weighting of each assessment of the importance
of IT processes, namely:

a. For the assessment is very important given the value 4
b.For the assessment is Somewhat important given the value 3

c. For the assessment is not important given the value 2

d. For the assessment was not sure given the value 1

The results of the weighting calculation is then performed in
such a manner as shown in figure 1 1 in order to get the final
score with the level of the index as follows (Kurniawan, Erva.
2011):

Table 5. Value dan Levels

Index of the Final Score

Levels

0-25

Not Sure

25-50

Not Important

50-75

Somewhat Important

75 - 100

Very Important

Interest Rate

Sum

—
t:

—

■3

E/3

n*4

11*3

n*2

ll*1

- %

c
- %

d.
- %

= Amount of Data)

Figure 11. The formula for measuring the rate of Interest

The results of the questionnaire data processing based on
the importance of IT processes at BKD show there on the
COBIT IT processes that have a value above 50. This value is

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012

then compared with the index scoring, and shows the level is
Somewhat important and very important. Assuming that the
process has a value above 50 is a process that must exist.
Process includes are DS1, DS2, DS3, DS4, DS5, DS6, DS7,
DS10, DS1 land DSD.

Interest Rate for IT Process on DS
Domain

120
100

I Interest Rate for IT
Processor! D5 Domain

S 9 10 11 12 13

Alignment of access rights with organizational
responsibilities

Reduce the number of new implementations delayed by
security problems

Reduce the number of incidents involving unauthorized
access, loss or destruction of information

Key Performance Indicator :

• Frequency of service interruption due to disruption to
business security systems

• Percentage of users who do not access in accordance
with the authority

• The number of active monitoring system with the ability

• Reduce the time to investigate security issues

• The time lag between detection, reporting and action on
security incidents

• The number of days of training on IT security awareness

Figure 12. Score for each IT Process

F. Determination of CSF, KPI and KGI

Based on rank of interest rates, then selected five IT
processes, namely DS1, DS5, DS7, DS10, DS11 with the
highest score should be given guidance on IT governance
model of CSF, KPI and KGI. Here's an example of CSF, KPI,
KGI to DS2 (COBIT Management Guidelines, 2000; 70)

Table 6. an Example for CSF, KPI, KGI to DS5 COBIT

Process Name :

DS5 Ensuring Security System

Business Target:

Minimizing the impact of security vulnerabilities and
incidents and maintain the integrity of information and
processing infrastructure

IT process objectives:

Monitoring, detecting, reporting and resolving security
vulnerabilities, incidents and defining IT security policies,
plans and procedures.

Critical Success Factor :

• Management of user authorization

• Management of disruption to system security

• The overall security plan is developed that includes
building awareness, establish clear policies and
standards, identifying and implementing sustainable cost
savings, as well as defining the monitoring and
enforcement process

• There is awareness that a good security plan must take
time to develop

• Management and staff have a general understanding of
security requirements, vulnerabilities, threats and
understand and accept responsibility for the security of
self

• A third party evaluation of security policies and
architecture is periodically performed

Key Goal Indicator :

• Decreasing the amount of disruption to the systems that
affect business services

G. Risk of Findings and Recommendations of the Substantive
Test

Substantive testing performed to meet the sufficiency of
the evidence is by doing some of the review to obtain a
conviction of BKD conformity with the conditions in the state
is supposed to happen. Tests performed include the control of
security management, control of SIMPEG applications, input
control, process controls, output controls and database control.

Table 7. Risk and Recommendation Based on Substantive Test Findings

Risk

Recommendation

Control of Security Management

There are still some staff who
bring food and drinks to the
server room or put them near
the computer equipment will
lead to a lack of security on the
management of facilities and
physical environment can harm
and even damage the computer.

In the absence of disaster
recovery plan in the application
system SIMPEG, this will result
in losses such as loss of data and
sluggish handling when the
system is susceptible to
interference.

BKD in the firm should
establish policies

requiring staff not to
bring food and drinks
into the server room or
near computer

equipment

SIMPEG application
system should come
equipped with a
disaster recovery plan
to prevent bad things
happen when the data
processing.

Management Control Applications

There are no age restrictions on
the password system will cause
the password easily recognized
by people who are not
responsible.

With no limits on the system
error in inputting the login
access (password and

username), it will provide
convenience for people who do
not have the authority to access

BKD should create a
policy that requires
staff to change
passwords periodically.

SIMPEG application
system should provide
limits in inputting
system login access.
Inputting errors should
be limited to 3 times, if

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

into the application system
SIMPEG.

it passes this limit then
the system will
automatically exit the
application.

Input Control

Result of the absence of color
changes on the display screen if
the data inputting errors occur,
then the user is not aware of any
errors when inputting, thus
reducing the effectiveness and
efficiency of users' work.

SIMPEG application system
that does not provide
functionality for back-up
warning will result in user
neglectful and at high risk for
loss of data.

SIMPEG application
system should be
equipped with a color
change of the display
screen in case of
inputting errors.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, 2012
that resources can be better utilized and will establish a
standard model for the process.

The results for substantive test are BKD already has the
documentation to third parties (vendors) and documentation
are clear procedures for managing SIMPEG. But there are
some things you may need to be added to the application, such
as the lack of backup, recovery, an automated help menu in
SIMPEG and password are not encrypted.

Should the warning be
added to the

application system data
has not been in the
back-up to avoid the
loss of corporate data.

Output Control

The absence of regular reporting
procedures demand or request a
new report in the application
system SIMPEG will result in
the timely distribution of reports
to be reduced.

Procedures should be
added routinely request
or demand for new
reports that are
programmed into the
application system

SIMPEG

Process Control

No data log in the application
system causes every process
that goes unrecorded, so
tracking it is difficult to do if
something goes wrong

SIMPEG application
system should be added
to the log data also
needs to control every
process that occurs.

Database Control

The absence of policy on
handling control file, this causes
the control to data and data
storage media in the event of a
malfunction of data is
inefficient.

Should implementation
a policy on handling of
the file system so that
control can be done
with back-up recovery
quickly to avoid the
risk of data loss.

V. CONCLUSION

Maturity level analysis shows that the whole process of IT
in DS domain is mapped at level 3, which means management
maturity level in BKD used COBIT 4. 1 is defined. This shows
that not a single information technology governance processes
that already meet. The whole process still has a gap that must
be covered. To achieve the expected level of maturity, a
number of rules, policies, recommendations and suggestions
for improvement of information technology governance model
has been successfully created.

The analysis of questionnaire about the importance of these
processes are feasible given the initial step for the
development of IT models include DS1, DS2, DS5, DS6, DS7,
DS10 and DS1 1 with the determination of CSF, KPI and KGI.
Using these guidelines and indicators, the process of IT
governance can be directed and driven by good information so

References

[I] Djatmiko,B. 2007. "Information Systems Audit Process to Assess
Service Delivery and Support Information Using the COBIT
Framework". Thesis. Bandung: ITB.

[2] BKD. 2011. "SIMPEG-BKD". Bali.

[3] Dwiani, R. 2010. "IT Governance Implementation Using COBIT 4.1

Fremework" Thesis. Jakarta: Indonesia University.
[4] Gondodiyoto, S. 2007. Information Systems Audit + COBIT Approach.

Jakarta: Mitra Wacana Media.
[5] IT Governance Institute Team. 2007. COBIT 4.1. USA: IT Governance

Institute.
[6] IT Governance Institute Team. 2000. COBIT Management Guidelines.

USA: IT Governance Institute.
[7] IT Governance Institute Team. 2000. COBIT Implementation Tool Set.

USA : COBIT Steering Committee and the IT Governance Institute.
[8] IT Governance Institute Team. 2000. COBIT 3 rd Framework. USA :

COBIT Steering Committee and the IT Governance Institute..
[9] Kurniawan, E. 2011. Evaluation of IT Governance Using COBIT

Framework Case Study: Provincial Government of Yogyakarta.

Yogyakarta: UGM.
[10] Nurharyanto, Ak. 2009. Auditing. Bogor : JFA Training Certification

Experts Exchange Formation Auditor.

[I I] Pederiva, A. 2003. The COBIT Maturity Model in a Vendor Evaluation
Case. Information System Control Journal Volume 3.

[12] Sarno, Riyanarto. 2009. Audit Systems & Information Technology. ITS
Press : Surabaya.

AUTHORS PROFILE

I Made Sukarsa, ST, MT is a lecturer who worked at Faculty of
Engineering, Information Technology Studies Program, Udayana University.

Maria Yulita Putu Dita is a student at Faculty of Engineering, Information
Technology Studies Program, Udayana University. Her research interest on
information systems audit using COBIT framework to get bachelor degree.

I Ketut Adi Purnawan, ST, M.Eng. is lecturer at Faculty of Engineering,
Information Technology Studies Program, Udayana University.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Intrusion Detection and Prevention Response based on
Signature-Based and Anomaly-Based: Investigation

Study

Homam El-Taj
Fahad Bin Sultan University
Tabuk, Saudi Arabia.
heltaj@ fbsu.edu. sa

Firas Najjar.
VTECH - LTD.

Riyadh, Saudi Arabia.
firas@vtech-sys.com

Hiba Alsenawi

Fahad Bin Sultan University

Tabuk, Saudi Arabia.

Habdelhakim@fbsu.edu.sa

Mohannad Najjar
Tabuk University
Tabuk, Saudi Arabia,
najj ar@ut.edu. sa

Abstract: One of the fundamental topics in network Signature
security is to detect intrusions and prevent them from exposing or
destroying important information, or breaking down systems.

In these systems the main problem is how to insure the abnormal
activity is a harmful activity and what the prop irate response to
stop the attack without affecting the whole process of the systems,
because wrong response may affect the system more than the
attempted intrusion, and because most organizations try to detect
every intrusion, they examine every suspicious event; which means
that more malicious events are detected but more resources are
needed to differentiate actual intrusion from false malicious events.
Also the Response for known attacks is accurate to stop them,
because you know every step of the attacks and you know how to
stop them, but when facing anomalies it's not clear is it attack and
what the best way to response without affecting the whole system.

In this paper we will present the IDPS (Intrusion detection and
prevention) techniques and their efficiency in preventing
intrusions.

Keywords -.Network security, Intrusion Detection and Prevention
Response, False Positive, False negative

I. Introduction
Nowadays most commercial and government information
systems are connected through the Internet, which will expose
them to avenues of attacks. This becomes very accurate because
of the increasing number of computer users, where the computer
became an essential tool for life style. With it people can see
news, send emails, pay by credit cards, study, and they can do
many other activities.

These systems must be protected from unauthorized access
that may expose critical information, by detecting any suspicious
anomalies in the network traffic patterns due to Distributed
Denial of Service (DDoS) attacks, worm propagation[l,2],
viruses, Trojans and other kinds of malicious programs that
introduce more panic into network society. Because of all this
danger, securing such networks infrastructure has become a
priority for most researchers.

In order to respond to this increasing threat the Information
Technology security industry provides a range of tools known as
vulnerability assessment tools as well as IDPS.

One of the major concerns of IDPS is to make sure the
detection of intrusion and reporting it, once the detection is
reliable, next step will be to protect the network (Prevention).

The major weakness in the IDS is the guarantee of intrusion
detection and the right response to stop the intrusion that will
make (IPS). This is the reason why in many cases IDSs are used
together with a human expert. As Ahmed Patel, Qais Qassim,
Christopher Wills[3] mentioned a well trained staff and analysts
are required to continuously monitor the system to protect
organizations critical information from attacks.

In this way, IDS is actually helping the network security
officer and it is not reliable enough to be trusted on its own. The
reason is the in ability of IDS systems to detect the new or altered
attack patterns. Although the latest generation of the detection
techniques has significantly improved the detection rate, still
there is a long way to go[3].

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

On the hand IDPSs help to automate the response for system
violation. And because most attacks have sequence of steps,
IDPSs try to stop the attacker to move to next step.

II.

IDPS Intrusion detection and prevention system

Computer networks systems can be susceptible to many kinds
of attacks. Securing such networks is a priority by detecting these
attacks. IDS is as one of the main tools used for this propose. IDS
is considered to be the first line of defense for any security
system.

A. History

Before IDPS was known the process of detecting intrusions
and responding for such intrusions was done manually, the
system administrator was reading all system logs trying to
detect abnormal activity. This approach took a lot of time and
effort, and just specialized people could do it. Therefore the
process of detecting and preventing intrusion had to be done
automatically [4]. However not every anomaly can be
considered as an attack or intrusion, and any wrong response
will affect whole system.

It is necessary to point out the early IDPS Software (not
Systems) was mostly individually developed, programmed and
not widely spread, as only very few organizations needed this
kind of technology before the dawn of the Internet age [4] .

B. Definition

National Institute of Standards and Technology (NIST) [5]
define (IDSs) as the process of monitoring the events occurring
in a computer system or network and analyzing them for signs
of possible incidents, which are violations or imminent threats
of violation of computer security policies, acceptable use
policies, or standard security practices.

NIST defines Intrusion prevention as the process of
performing intrusion detection and attempting to stop detected
possible incidents, on other words intrusion prevention (IPSs)
system is an active intrusion detection system.

IDPSs will monitor computer system environment,
identifying problems with the security polices, notify system
administrator of abnormal event and security violation and try
to stop intruders making successful attack by stopping the
attack itself or by changing the configuration of security
environment like routers.

C. IDPS technologies

The types of IDPS technologies as mentioned in NIST [5]
depend on the type of events that they monitor and the way
which they are deployed:

• Network-Based: monitors network traffic for particular
network segments and analyzes the network and
application protocol activity to identify suspicious
activity. The main disadvantage of this technology it
can't be used if encrypted communication is allowed

• Wireless: monitors wireless network traffic and
analyzes it to identify suspicious activity involving the
wireless networking protocols themselves. One of the
disadvantage it cannot identify suspicious activity in the
application or higher-layer network protocols.

• Network Behavior Analysis (NBA): Monitor network
traffic to identify threats that generate unusual traffic
flows, such as distributed denial of service (DDoS)
attacks, certain forms of malware, and policy violations.

• Host-Based: Must be deployed on each protected
machine (server or workstation) to monitors the
operating system, applications, the host specific network
traffic, and analyze the data to that machine such as
system log files, audit trails and file system changes.
Main disadvantages are the installation on every single
host and the adaptation to the different platforms and
operating systems.

Most IDPS technologies use multiple detection methodologies,
either separately or integrated to provide more broad and
accurate detection.

D. IDPSs Methodologies

The common methodologies that IDPSs uses to identify
threats as NIST [5] mentioned are:

• Signature-based, (also denoted as misuse-based) seek
defined patterns, or signatures, within the analyzed data.
This methodology very effective detecting known
threats and have small number of wrong detection, but
ineffective detecting new threats or unknown one and
the set of signature must be constantly update manually
to new threat. For this purpose, there must be a
signature database corresponding to known attacks.

• Anomaly-based, detectors attempt to estimate the
"normal" behavior of the system to be protected, and
generate an anomaly alert whenever the deviation
between a given observation at an instant and the
normal behavior exceeds a predefined threshold, it can

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

be very effective for detecting new threats, but it
generates many False Positive alerts.

E. False Positive, False Negative

False positive occurs when your IDPSs generates an alert
from normal user or system activity. If IDPSs generates too many
false positives, then it will lose confidence in the capability of
your IDPS to protect your network.

False negative occurs when an attack occurs against your
system and the IDPSs fail to detect and notify the systems
administrator. IDPSs must never generate false negatives. Most
companies prefer the IDPSs generate more false positives rather
than generating any false negatives [5].

Historically, IDPSs have been associated with high rates of
false positives and false negatives. Most of the early technologies
relied primarily on signature-based detection, which by itself is
accurate only for detecting relatively simple well-known threats.
Newer technologies use a combination of detection methods to
increase accuracy and the breadth of detection, and generally the
rates of false positives and false negatives have declined [6].

III. IDS Intrusion Detection
Now we will make quick survey about the most popular
IDSs systems based on the events that they are monitored and the
methodology that they are using.

A. Host and network based

Intrusion may occur through hosts or networks, host-based
Intrusion Detection (HIDS) examines many operations on the
system, including function calls, files accessed, and so on. One
common method for detecting anomalous user behavior is to
establish a baseline of the operations that a user normally
performs on the system. Then by monitoring deviations from the
baseline, you can detect potentially malicious activity.

A network-based Intrusion Detection system (NIDS)
monitor network traffic on packet level. The components are the
network based IDS software, running on a dedicated host,
connected to the network traffic with a network interface.

NIDS capture the network traffic from the wire as it travels
to a host, then the captured packets analyzed and compare to a
particular signature or for unusual or abnormal behaviors.
Several sensors are used to sniff the packets on network which
are basically computer systems designed to monitor the network
traffic. If any suspicious or anomaly behavior occurs then they
trigger an alert and pass the message to the central computer
system or administrator (which monitors the IDS).

Host-based and network-based IDS normally maintain a
database of system objects and also stores the system's normal
and abnormal Behavior [7]. The database contains important
information about system files, behavior and objects such as

attributes, modification time, size, etc. If any suspicious or
anomaly behavior occurs then it generates an alert and takes
some appropriate response against detected threat or attack.

Researchers make studies on Host IDS and network IDS or
both, SANS Institute [6] is example of using two technology,
host-based IDS and network-based IDS, they Introduce software
based solution which detects and protects the system from
network layer up to application layer by known and unknown
attacks. This software has great flexibility to set different type of
filtering rules. The major drawback of this system is its high rate
of false-positives. A lot of time and trained staff is required to
monitor the IDS.

Muhammad Shibli and Sead Muftic [7] Provide host-based
IDS to secure mobile agent. Secure mobile agent monitors the
system, processes the logs, detects the attacks, and protects the
host by automated real time response. Major disadvantage is that
if the target of the attackers is mobile agent then it will be
difficult to protect the system from being hacked. So it needs to
adopt some security infrastructures for the protection of mobile
agent.

Another research on host-based IPS done by M. Laureano,
C. Mazierol, and E. Jamhour [8], they proposed architecture to
protect Host-based IDS through virtual machine by observing the
system behavior or monitor the system inside a virtual machine.
This technique is efficient, duplication of real operating system,
invisibility and inaccessibility to intruders. Multiple virtual
machines can run simultaneously on same hardware.

David Wagner and Paolo Soto. [9] Examined technique on
host-based which shows that how application interacts with the
operating system and how to defraud IDS and make intrusion
without detection, by using the technique of sequence matching,
inserting malicious sequence.

B. Artificial intelligence intrusion detection

Application of the artificial intelligence is widely used for
the IDS purpose. Researchers have proposed several approaches
in this regard. Some of the researchers are more interested in
applying rule based methods to detect the intrusion.

1) A Bayesian network

A Bayesian network is a probabilistic graphical model (a
type of statistical model) that represents a set of random variables
and their conditional dependencies via a directed acyclic graph.

Bayesian network methodology has a unique feature. For a
given consequence, using the probability calculations Bayesian
methodology can move back in time and find the cause of the
events.

This feature is suitable for finding the reason for a particular
anomaly in the network behavior. Using Bayesian algorithm,
system can somehow move back in time and find the cause for

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

the events. This algorithm is sometimes used for the clustering
purposes as well. D. Bulatovic, D. Velasevic [10] and M.
Bilodeau, D. Brenner [11] are examples of this approach.
Kruegel C, Mutz D., Robertson W. and Valeur F. Bayesian
pointed out , a serious disadvantage of using Bayesian networks
is that their results are similar to those derived from threshold-
based systems, while considerably higher computational effort is
required [12].

2) Data Mining and IDS

Data mining (DM), also called Knowledge-Discovery in
Database, is the process of automatically searching large volumes
of data for patterns using association rules.

Data Mining used in many computational techniques from
statistics, information retrieval, machine learning and pattern
recognition. The main function of data mining is to classify the
captured events, as normal, or malicious, or as a particular type
of attack [12, 13, 14].

Data mining can be done online [15] (real time) or offline
[14, 16, 17, 18, 19].

Many researcher uses data mining to solve the intrusion
detection problem. Researchers such D. Barbara, J. Couto, S.
Jajodia, N. Wu, Ken. Yoshida, Lee W., and Stolfo S.J. [20, 21,
22,23].

3) Markov

Markov chain is a set of states that are interconnected
through certain transition probabilities, which determine the
topology and the capabilities of the model. During a first training
phase, the probabilities associated to the transitions are estimated
from the normal behavior of the target system. The detection of
anomalies is then carried out by comparing the anomaly score
(associated probability) obtained for the observed sequences with
a fixed threshold.

A hidden Markov model (HMM) is a statistical model
where the system being modeled is assumed to be a Markov
process with unknown parameters, and the challenge is to
determine the hidden parameters from the observable parameters.
The extracted model parameters can then be used to perform
further analysis, for example for pattern recognition applications.
A HMM can be considered as the simplest dynamic Bayesian
network. Example of researcher work on that field Mahoney
M.V., Chan P.K Yeung DY, Ding Y. Este'vez-Tapiador J.M.,
Garci'a-Teodoro P., and Di'az-Verdejo J.E [24, 25, 26].

4) Fuzzy Logic

Fuzzy logic is derived from fuzzy set theory under which
reasoning is approximate rather than precisely deduced from
classical predicate logic.

Fuzzy logic is very appropriate for using on intrusion
detection [27]; one reason is that usually there is no clear
boundary between normal and anomaly events. The use of
fuzziness of fuzzy logic helps to smooth the abrupt separation of
normality and abnormality.

John E. Dickerson, Julie A. Dickerson, Susan M. Bridges,
M. Vaughn Rayford M. Botha and R. von Solms are examples of
those researchers that follow this approach [28, 29, 30]. Some
researchers even used a multidisciplinary approach, for example,
Gomez et al. [31] have combined fuzzy logic, genetic algorithm
and association rule techniques in their work. Cho [32] reports a
work where fuzzy logic and Hidden Markov Model (HMM) have
been deployed together to detect intrusions. In this approach
HMM is used for the dimensionality reduction.

Although fuzzy logic has proved to be effective,
especially against port scans and probes, its main disadvantage is
the high resource consumption involved. On the other hand, it
should also be noticed that fuzzy logic is controversial in some
circles, and it has been rejected by some engineers and by most
statisticians, who hold that probability is the only rigorous
mathematical description of uncertainty[30].

5) Artificial Neural Network

An artificial neural network (ANN) is a mathematical
model or computational model that is inspired by the structure
and/or functional aspects of biological neural networks. Neural
networks have been adopted in the field of anomaly intrusion
detection, mainly because of their flexibility and adaptability to
environmental changes and to provide an unsupervised
classification method to overcome the curse of dimensionality for
a large number of input features. Since the system is complex
and input features are numerous, clustering the events can be a
very time consuming task. Using the Principle Component
Analysis (PCA) or Singular Value Decomposition (SVD)
methods can be an alternative solution [33]. However, if not used
properly both of these methods can become computationally
expensive algorithms. At the same time, reducing the number of
features will lead to a less accurate model and consequently it
will reduce the detection accuracy [34, 35, 36].

IV.

IPS Intrusion prevention

Seeing the intrusion is thing and stopping it is another thing,
seeing the attack occur will terrify the users of IT security, well
trained IT security must deal with such attacks. If the highest
priority of IT security is to stop intrusion then IDS just like an
alert.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

IDS detect abnormal activity and generate alerts, but it do
nothing to break the attack, just notify the system administrator,
the notified administrator must respond quickly to block the
attack, but for big companies IDSs will generate thousand to
millions of alerts per day, most of them are false positive ones, It
will be very difficult for the administrator to analyze all alerts,
and make the right response for each one of them, so most
intrusion are detected after the attacks. [5]

On other hand Intrusion Detection and Prevention System
(IDPSs) Works like Intrusion Detection System (IDS) it detect
abnormal activity but the only difference that IDPSs can respond
to a detected threat by attempting to prevent it from succeeding,
the main problem is: what is the exact repose for a specific threat,
wrong response may affect the system more than the intruder will
[5].

Many organizations choose to decrease false negatives at
the cost of increasing false positives, which means that more
malicious events are detected but more analysis resources are
needed to differentiate false positives from true malicious events.
The major problems in the IDPSs is the guarantee of the intrusion
detection, and make the right response to stop the attack, this is
the reason why in many cases IDPSs are used together with a
human expert.

Anomaly-based IDPSs produce many false positive alert,
this will need a lot of calculating time and resources, [37]
discusses the foundations of the main Anomaly-Based intrusion
detection technologies, together with their general operational
architecture, and provides a classification for them according to
the type of processing related to the "behavioral" model for the
target system, there is many techniques enhancing the detection
face and decreasing the false positive, [38] Make a review of
using computational intelligence in intrusion detection system,
characteristics of computational intelligence (CI) systems, such
as adaptation, fault tolerance, high computational speed and error
resilience in the face of noisy information.

Most researchers study the behavior of the intruders and
attacks, and generate alerts to notify the system administrators,
[39] worked on finding attack steps that are correlated in an
attack scenario. They use algorithm to correlate multi-step cyber
attacks in real time and constructing attack scenario system based
on modeling multi-step cyber attacks. When generating alerts,
the algorithm turns them into corresponding attack models based
on the knowledge base and correlates them, whether alert or not
is based on the weighted cost in the attack path graph and the
attack degree of the corresponding host. And attack scenarios can
be constructed by correlating the attack path graphs.

Ning P., Cui, Y., Reeves, D., Xu, [40] introduce framework
that correlates alerts on the basis of prerequisites and

consequences of attacks. To make successful attack you must
actualize necessary conditions which called Prerequisites. And
the results of the attacks called consequences. The framework
matches the consequences of some prior alerts with the
prerequisites of some new ones, to generate attack scenarios
which are the combination of steps that attackers use in their
attacks. They show the logical connection between otherwise
independent IDS alerts. A successful attack arises from sequence
of scenarios and each scenario may contain sub-scenarios. Then
they represent each attack scenarios as a hyper alert correlation
graph, which uses nodes to represent alerts and edges to represent
the relationships between the alerts.

Dr. Eric Cole [41] presents a method to profile and identify
attackers by analyzing attack scenarios and profile attributes. It
creates profiling for the attacker techniques and steps which will
be used by the intrusion detection system, and to predict attacker
behavior.

V. Conclusion

Anomaly-based IDS is the best way to detect novel attacks, but it
leads to high false positive alert which decrease the reliability of
the IDS, usually for that a hybrid approach is used. In the hybrid
approach, the signature-based approach is used together with the
anomaly-based approach, in this way; the second approach is
mostly used to detect novel attacks while the accuracy of the first
approach (signature based approach) will provide a reliable
detection for the known attacks.

IDPS has additional features to secure computer network system.
The additional features identifying and recognizing suspicious
threat trigger alert, event notification, through responsible
response. In this preliminary observation from previously
researchers, a hybrid technique is one good solution to classify
and detect intrusion threat. Proposed hybrid IDPS takes the
advantages to increase accuracy and precision normal or
suspicious threat. For novel attacks (Anomaly-based detection) it
will be difficult to choose the right response specially not all
anomalies are threats.

Making response for the attacks detected by Signature-based
detection is accurate, but for anomaly-based there is no clear
response, deep studies on attacks behavior and what the best
response to stop them without affecting the whole system will
help the response for anomaly-based detection, specially most
attacks have multi steps to achieve their goal, there must be
technique that study attacks behavior and compare the sequence
of events with these attacks' behaviors to predict the
consequence of next step, and make appropriate response
minimize and prevent loses.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

References

[I] Christos Douligeris, Aikaterini Mitrokotsa, "DDoS attacks and
defense mechanismsxlassification and state-of-the-art" .Computer
Networks:The International Journal of Computer and
Telecommunications Networking, Vol. 44, Issue 5 , pp: 643 - 666,
2004.

[2] Z. Chen, L. Gao, K. Kwiat, "Modeling the spread of active
worms, Twenty" Second Annual Joint Conference of the IEEE
Computer and Communications Societies (INFOCOM), Vol. 3, pp.
1890 1900,2003.

[3] Ahmed Patel, Qais Qassim, Christopher Wills. "A survey of intrusion
detection and prevention systems", Information Management &
Computer Security Journal (2010).

[4] Allen J, Christie A, Fithen W, McHugh J, Pickel J, Stoner E. "State
of the practice of intrusion detection technologies", Carnegie Mellon
University Technical Report CMU/SEI-99- TR-028; 2000. CSI/FBI
annual computer crime and security survey.

ComputerSecurityInstitute,http://www. gocsi.com.

[5] Karen Scarfone,Peter Mell. "Guide to Intrusion Detection and
Prevention Systems (IDPS. National Institute of Standards and
Technology(NIST),Feb 2007

[6] Host Intrusion Prevention Systems and Beyond, SANS Institute
(2008).

[7] Muhammad Awais Shibli, Sead Muftic. "Intrusion Detection and
Prevention System using Secure Mobile" Agents, IEEE International
Conference on Security & Cryptography (2008).

[8] M. Laureano, C. Mazierol, E. Jamhour. "Protecting Host-Based
Intrusion Detectors through Virtual" Machines.The International
Journal of Computer and Telecommunications Networking (2007).

[9] David Wagner, Paolo Soto. "Mimicry Attacks on Host Based
Intrusion" Detection Systems, 9th ACM Conference on Computer
and Communications Security (2002).

[10] D. Bulatovic and D. Velasevic, "A distributed intrusion detection
system based on bayesian alert networks," Lecture Notes in
Computer Science (Secure Networking CQRE (Secure) 1999), vol.
1740,pp. 219-228, 1999.

[II] M. Bilodeau and D. Brenner, "Theory of multivariate statistics.
Springer". Verlag : New York, 1999. Electronic edition at ebrary, Inc.

[12] Kruegel C, Mutz D., Robertson W., Valeur F. Bayesian "event
classification for intrusion detection". In: Proceedings of the 19th
Annual Computer Security Applications Conference; 2003.

[13] Ghosh, A. K., A. Schwartzbard, and M. Schatz, "Learning program
behavior profiles for intrusion detection", In Proc. 1st USENIX, 9-12
April, 1999

[14] Kumar, S., "Classification and Detection of Computer Intrusion",
PhD. thesis, 1995, Purdue Univ., West Lafayette

[15] [5] D. Barbara, N. Wu, and S. Jajodia, "Detecting novel network
intrusions using bayes estimators," in Proceedings of the First SIAM
International Conference on Data Mining (SDM 2001), Chicago,
USA, Apr. 2001.

[16] W. Lee, S.J.Stolfo et al, "A data mining and CIDF based approach
for detecting novel and distributed intrusions", Proc. of Third
International Workshop on Recent Advancesin Intrusion Detection
(RAID 2000),Toulouse, France.

[17] Lee, W., S. J. Stolf'o, and K. W. Mok, "A data mining framework for
building intrusion detection models," In Proc. of the 1999 IEEE
Symp. On Security and Privacy, Oakland, CA, pp. 120132. IEEE
Computer Society Press, 9-12 May 1999

[18] Eric Bloedorn et al, "Data Mining for Network Intrusion Detection:
How to Get Started," Technical paper, 2001.

[19] Singh, S. and S. Kandula, "Argus a distributed network intrusion
detection system," Undergraduate Thesis, Indian Institute of
Technology, May 2001.

[20] D. Barbara, J. Couto, S. Jajodia, and N. Wu, "Special section on data
mining for intrusion detection and threat analysis: Adam: a testbed
for exploring the use of data mining in intrusion detection," ACM
SIGMOD Record, vol. 30, pp. 15-24, Dec. 2001.

[21] D. Barbara, N. Wu, and S. Jajodia, "Detecting novel network
intrusions using bayes estimators," in Proceedings of the First SIAM
International Conference on Data Mining (SDM 2001), Chicago,
USA, Apr. 2001.

[22] Ken. Yoshida, "Entropy based intrusion detection," in Proceedings of
IEEE Pacific Rim Conference on Communications, Computers and
signal Processing (PACRIM2003), vol. 2, pp. 840-843. IEEE, Aug.
2003.

[23] Lee W., Stolfo S.J. Data mining approaches for intrusion

detection. In: Proceedings of the 7th USENIX Security

Symposium(SECURITY-98); 1998. p. 79-94
[24] Mahoney M.V., Chan P.K. "Learning nonstationary models of

normal network traffic for detecting novel attacks". Proceedings of

the Eighth ACM SIGKDD; 2002. p. 376-85.

[25] Yeung DY, Ding Y. "Host-based intrusion detection using dynamic
and static behavioral models. Pattern Recognition' 2003;36(1) 229-
43.

[26] Este'vez-Tapiador J.M., Garci'a-Teodoro P., Di'az-Verdejo J.E.
"Detection of web-based attacks through Markovian protocol
parsing" Proc. ISCC05; 2005 p. 457-62.

[27] J.E. Dickerson, J. Juslin, O. Loulousoula, and J. A. Dickerson,
"Fuzzy Intrusion Detection", IFSA World Congress and 20th North
American Fuzzy Information Processing Society (NAFIPS)
International Conference, 2001, ppl506-1510.

[28] John E. Dickerson and Julie A. Dickerson, "Fuzzy network profiling
for intrusion detection," Proceedings of NAFIPS 19th International
Conference of the North American Fuzzy Information Processing
Society, pp. 301-306, Atlanta, USA, July 2000.

[29] Susan M. Bridges and M. Vaughn Rayford, "Fuzzy data mining and
genetic algorithms applied to intrusion detection," Proceedings of the
Twenty-third National Information Systems Security Conference.
National Institute of Standards and Technology, Oct. 2000.

[30] M. Botha and R. von Solms, "Utilising fuzzy logic and trend analysis
for effective intrusion detection," Computers & Security, vol. 22, no.
5, pp. 423^134,2003.

[31] J. Gomez and D. Dasgupta, "Evolving fuzzy classifiers for intrusion
detection," Proceedings of the 2002 IEEE Workshop on the
Information Assurance, West Point, NY, USA, June 2001.

[32] S. B. Cho, "Incorporating soft computing techniques into a
probabilistic intrusion detection system," IEEE TRANSACTIONS
ON SYSTEMS, MAN, AND CYBERNETICSPART C:
APPLICATIONS AND REVIEWS, vol. 32, pp. 154-160, May 2002.

[33] M. Analoui, A. Mirzaei, and P. Kabiri, "Intrusion detection using
multivariate analysis of variance algorithm," Third International
Conference on Systems, Signals & Devices SSD05, vol. 3,
Sousse,Tunisia, Mar. 2005. IEEE.

[34] Ste. Zanero and Sergio M. Savaresi, "Unsupervised learning
techniques for an intrusion detection system," Proceedings of the
2004 ACM symposium on Applied computing, pp. 412^-19, Nicosia,
Cyprus Mar. 2004. ACM Press.

[35] H. Gunes Kayacik, A. Nur Zincir-Heywood, and Malcolm I.
Heywood, "On the capability of an som basedintrusion detection
system," Proceedings of the International Joint Conference on Neural
Networks, vol. 3, pp. 1808-1813. IEEE, IEEE, July 2003.

[36] J. Z. Lei and Ali Ghorbani, "Network intrusion detection using an
improved competitive learning neural network," Proceedings of the
Second Annual Conference

[37] P. Garcia-Teodoro, J. Dian-Verdejo, G. Macia-Fernandez, and E.
Vazquez, "Anomaly-based network intrusion detection: Techniques,
systems and challenges," Computer & Security, vol. 28, 2009, pp.
18-28.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

[38] S.X. Wu and W. Banzhaf, "The use of computational intelligence in
intrusion detection systems : A review," Applied Soft Computing,
vol. 10, 2010, pp. 1-35.

[39] Modeling Zhijie Liu; Chongjun Wang; Shifu Chen; Nat."Correlating
Multi-Step Attack and Constructing Attack Scenarios Based on
Attack Pattern". Information Security and Assurance, 2008. ISA
2008. International Conference

[40] Ning, P., Cui, Y., Reeves, D., Xu, D." Techniques and Tools for
Analyzing Intrusion Alerts". ACM Transactions on Information and
System Security, Vol. 7, No. 2. May 2004.

[41] Dr. Eric Cole "Constructing Attack Scenarios for Attacker Profiling
and Identification"

56 http://sites.google.com/site/ijcsis/

ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Extended Sakai-Kasahara Identity-Based
Encryption Scheme to Signcryption Scheme

Hussein Khalid Abd-Alrazzaq 1

1 College of Administration and Economic -Ramadi, Anbar University,

Anbar, Iraq
hu_ albasri@yahoo. co. uk

Abstract — Identity-Based Signcryption (IBSC) is a better
approach would be to exploit the similarities between IBE
and IBS in order to provide a dual-purpose IB Encryption-
Signature (IBSE) scheme based on a shared infrastructure,
toward efficiency increases and security improvements. In
this paper describes a new identity-based signcryption
scheme built upon SK scheme. It combines the
functionalities of signature and encryption and it is prove
security in a formal model under computational
assumptions and in the random oracle model. As a result,
this paper propose a new secure identity-based
signcryption (IBSC) scheme that is also compare it with the
other from efficiency points of view.

Keywords: Public Key Cryptography, Identity-Based
Cryptography, Identity-Based signcryption, Sakai-Kasahara
IBE

I. Introduction

The notion of identity-based (IB) cryptography was
proposed by Shamir [1] as a specialization of public key
(PK) cryptography which dispensed with the need for
cumbersome directories, certificates, and revocation lists.
This concept have advantageous over the traditional
public key cryptosystems (PKC), it is used to eliminate
the complexity of using digital certificates to public key
register, where the public key of a user (in D3C) can be
derived from public information that uniquely identifies
the user Because of this, IB systems implement an
automatic directory with implicit binding, without the
need for costly certification and public key publication
steps. Although public keys can be computed by anyone
from public information, the corresponding private key
can only be extracted by a trusted authority called the
private key generator (PKG). The PKG has custody of a
master secret, which allows it to compute any private key
in the IB system. The PKG can be thought of as an
identity-based analog to the CA at the helm of a
traditional public key infrastructure [2] .

Identity-Based Signature schemes (IBS) have been
devised since 1984 But a satisfying Identity-Based
Encryption scheme (IBE) only appeared in 2001 by
Boneh and Franklin gave a practical Identity-Based
Encryption scheme that relies on the bilinear Diffie-

Hellman problem for its security. It uses a complicated
mathematical transformation called the Tate pairing [3].

The concept of public key signcryption schemes was
found by Zheng in 1997 [4]. The idea of this kind of
primitive is to perform encryption and signature in a
single logical step in order to obtain confidentiality,
integrity, authentication and non-repudiation more
efficiently than the sign-then-encrypt approach. Several
efficient signcryption schemes have been proposed since
1997 and a first example of formal security proof in a
formal security model was published in 2002 [5].
However, until 2002, none of these schemes were
identity based

II. Sakai-Kasahara Identity-Based
Encryption (SK-IBE)

Sakai-Kasahara IBE an example of the family of
"exponent inversion" schemes, in which a private key of

the form g > a is used to decrypt a ciphertext. SK-IBE is a
secure IBE scheme based on the k-BDHI problem, while.
The security of BF-IBE is based on the BDH problem.
The advantage of SK-IBE is that it has better
performance than BF- IBE, particularly in encryption.
SK-IBE is faster than BF-IBE in two aspects. First, in the
Encrypt algorithm of SK-IBE, no pairing computation is
required because e (P 1 , P 2 ) can be pre -computed. Second,
in operation of mapping an identity to an element in Gl
or G2, the map to point algorithm used by BF-IBE is not
required. Instead of that, SK-IBE makes use of an
ordinary hash-function, this avoids a modular
exponentiation. Therefore SK-IBE provides an attractive
performance; also SK-IBE is secure against adaptive
chosen ciphertext attacks in the random oracle model
based on the g-BDHI assumption [6].

In these schemes, a string representing an identity is
hashed to an integer that is then used in the encryption
and decryption operations. An SK-IBE scheme consist
four steps [7]:

• Setup takes as input k, and returns a master public
key M pk and a master secret key M sk .

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

• Extract takes as input M pk , M sk and ID A 6 {0,1} an
identifier string for entity A, and returns the
associated private key dA.

• Encrypt takes as input M pk , ID A and a message m ,
and returns a ciphertext C.

• Decrypt takes as input M pk , ID A , d A and C, and
returns the corresponding value of the plaintext m

III. The Identity-Based Signcryption
Primitive

An Identity-Based Signcryption scheme, or IBSC,
comprises four algorithms: Setup, Extract, Signcrypt, and
Unsigncrypt. In a (two-layer) IBSC with detachable
signature, the signcryption/unsigncryption algorithms are
the composition of explicit subroutines: Signcrypt =
Encrypt ° Sign and Unsigncrypt = Verify ° Decrypt.

In summary, Setup generates random instances of the
common public parameters and master secret; Extract
computes the private key corresponding to a given public
identity string; Signcrypt produces a signature for a given
message and private key, and then encrypts the signed
plaintext for a given identity (note that the encryption
routine may specifically require the signature as input);
Decrypt decrypts a ciphertext using a given private key;
Verify checks the validity of a given signature for a given
message and identity. Messages are arbitrary strings in
{0,1}*. the functions that compose a generic IBSC
scheme are as follows [2]:

• Setup: produces a pair (msk, mpk), where msk
is a randomly generated master secret and mpk
the corresponding common public parameters.

• Extract (mpk, msk, ID): On input ID,
computes a private key sk which corresponding
to the identity ID under (msk, mpk).

• Signcrypt (mpk,IDS,IDR,skS,m): The
sequential application of

- Sign (mpk,ID s ,sk s ,m): On input
(ID s ,sk s ,m), outputs a signature s, for
sk s , under mpk, and some ephemeral
state data r .

- Encrypt (mpk,ID R ,sk s ,m,s,r): On
input (ID R , sk s , m, s, r ), outputs an
anonymous ciphertext C, containing the
signed message (m,s) encrypted for the
identity ID R under mpk.

• Unsigncrypt (mpk, sk R , C ): The sequential
application of

- Decrypt (mpk,sk R ,C): On input
(sk R ,C), outputs a triple (ID s ,m,s)
(containing the purported sender identity
and signed message obtained by
decrypting C by the private key sk R under
mpk).

- Verify (mpk,ID s ,m ,s): On input
(ID s ,m ,s), outputs "true" or "false"
(indicating whether s is a valid signature
for the message m by the identity ID S ,
under mpk).

IV. Complexity Assumption

Strong Diffie-Hellman (q-SDH): For an integer
q,andx 6 z* ,P 6 G 1 , given (P,xP,x 2 P, ... , x q P)

computing [h — P) where h 6 z* is hard [5],

The Bilinear Diffie-Hellman Problem (BDHP):

letdn-L, (Gj r two groups of prime order q, e:^ X <& 1 -»
G T , be bilinear map, P be a generator of G t , The BDHP
is: given P, aP, bP, cP, where a, b c 6 Z p calculate
e(P,P) abc . Solving the BDHP is no more difficult than
calculating discrete logarithms in either G\ or Gj .If it can
find the value of c by calculating the discrete logarithm
of cP in G-l, then it can calculate e(aP,bP) c =
(e(P, P ) ab ) c = (e(P,P) abc or, if it can find the value of
c by calculating the discrete logarithm of e{P, cP ) =
e(P,P) c in <G r then it also calculate e(P,P) abc in a
similar way [3].

qr-Bilinear Diffie-Hellman Inversion Problem (q-
BDHB?): letdnj, G T two groups of prime order q,
e:G 1 X G 1 -* <G r , be bilinear map, P be a generator of

Gi. The 4-BDHIP is: given P, aP, a 2 P, ... , a q P,

i
calculate e(P,P )3 . Solving the g-BDHIP is difficult as
calculating discrete logarithms in either Gior Gj. if it can
find the value of a by calculating the discrete logarithm
of aP in G\, then it can calculate - and then

calculated (P, P )<j. Or if it can find the value of a by

calculating the discrete logarithm of e(P, aP) = e(P,P) a

i
in G^then it also calculate e(P,P )a in a similar way [6].

Decisional Bilinear DH Inversion Problem (q-
DBDHI): For an integer q,andx,r 6 z* ,P 2 6
G t ,P1 = xp(P 2 ),e ■■ G t X Gi -> G T , distinguishing
between the distributions (P 1 ,P 2 ,xP 2 ,x 2 P 2 , ... ,x q P 2 ,

e(Pi,P 2 ) 1/x ) and (P 1 ,P 2 ,xP 2 ,x 2 P 2 x"P 2 ,

e(P 1 ,P 2 ) r )ishard[6].

V. The Proposed Scheme

In this paper proposed identity-based signcryption
scheme through modification on Sakai-Kasahara IBE to
build efficient signcryption schemes. There are many of
authors have proposed various new identity-based
signcryption schemes, but this paper proposed new
scheme belong to the family of "exponent inversion"
schemes. This scheme acquires its power form the q-
bilinear Diffie-Hellman inversion problem which is
depending on the elliptic curve discrete logarithm.

Sakai-Kasahara
Signcryption (SK-IBSC)

Identity-Based

The proposed scheme involves three roles: the Private
Key Generator (PKG), the sender S, and the message
recipient R. It consists of four algorithms: Setup which is
used to generate public parameters used throughout
the life of the system, Extraction which used to generate
private key correspond with the user's identity, Signcrypt

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

which used to sign and encrypt the message by the
sender, and Unsigncrypt which used to verify and decrypt
the message by the recipient. The details of them are
described as below.

Setup: Let P be a generator of G\, Pick a random
s,a 6 s* and set Pi = sP, P 2 =aP. Additional
cryptography hash functions used to add chosen-
ciphertext security, as in SK IBE full scheme, //i:{0, 1}
-> 1 p , H 2 :G T -> {0, 1}" , H 3 : {0, 1}" — ►zj, H 4 :
{0,l} n ^{0,l} n .The master secret for SK-IBSC is 5
and a , the public parameters are: q (order of Wq), E/Wq, p
(Prime number: p I #E(Wq ), G] and G T are Cyclic group
where (P) and e((P,Py generated of them,
Respectively, e : G, X G, -> G T , n,P,P h P 2 , v=e(P,P) H b
H 2 , H 3 , H 4 ).

Extraction: Given a user's identity (ID), PKGC
generates the Private key corresponding to ID, as
following:

1. computes the hash value q ID = H\{ID)

2. computes the corresponding private key

u lrt =

s+qw
Signcrypt: the sender encrypts the message M with

recipient's ID (ID R ) as public key, and sign M with his

private key (d /Ds ) as in the following steps:

1. Compute a public key of recipient q IDR =
Hi(W R )

2. Pick a random x, a 6 z*

3 . Compute r = H 3 (M,a,x)

4. Compute U = r(P t + q WR P) = r(s + q IDR )P

5. Compute /C = H 2 (y r )

6. Compute z = a © K

7. Compute Y = xP

8. Compute W = xd IDs + zP 2

9. Compute C = M© a

10. send (C, W, U, z, Y) as ciphertext
Unsigncrypt: the recipient Input his/her private key

( d, DR ), ciphertext (C, W, U, z, Y) and sender's public key
(ID S ) to decrypt and verify, as in the following steps:

1. Compute a public key of sender q IDs = H t (ID s )

2. Compute K = H 2 (e(U, d IDR ))

3. Compute a = z © K

4. Compute M = C© a

5. Ifelw.q^P + P,) = e(Y,P)e(zP 2 ,q IDs P +
P 1 ) then the signature is true.

The consistency is easy to verify by the bilinearity of

the map. Indeed, We have e(W,q IDs P + P^ =
e(Y,P)e(zP 2 ,q 1Ds P + P 1 ) , where e(xd Ws ,q Ws P t ) =
e(Y,P)<md e(zP 2 ,q IDs P + P ± ) = e(zP 2 , q Ws P + P ± )
Anyone can be convinced of the message's origin by
using Z to retrieve the message and verify the signature
without error that is mean this message not modified. The
knowledge of the plaintext m is not required for the
verification.

B. Analysis of the Scheme

Correctness

K = H 2 (e(U,d, DR ))

= H 2 U(r(P 1 + qiDR P),— Pj

)

= H 2 (e(r(s + (?/D JP,^l-p))

= H 2 (e(rP,Py C " DR )* s+CIID R) = H 2 (e(P,Py
= H 2 (v r )

e(W,q Ws P + P x ) = e(xd IDs + zP 2 ,q Ws P + sP)
= e\x P + zaP,(q w<: + s)P)

s + q, D /

P, (q IDs + s)P) e(zaP, (q IDs + s)P)

= e{x

S + <JlD s

= e(P P) s+q ' D s s e(P p) za ^'D s +s )
= e{P,P) x §(p > py a ^iD S +s ^

e(Y,P)e(zP 2 ,q IDs P + P 1 )

= e(P,Pye(zaP,(q IDs +s)P)

= e(P,Py e(P,Py a ( qw s +s )

Security

Unforgeability: Since the signcryption produce of
modified the as the Sakai-Kasahara IBE scheme, forging
a ciphertext for any message m is equivalent to forge a
Sakai-Kasahara scheme, because the proposed scheme
based on the same computational problem. It has
unforgeability against adaptive chosen message attacks
(in the random oracle) assuming the q-BDHI problem is
hard. Therefore, the attacker needs to use the privet key
of the true sender if he wants to forge the original
signature. Because that impossible the attacker must

replace his identity form his private key P

S+qiD Attacker

with identity of original to general forged private key for
-P. In order do this process; the attacker

sender -

s+qiD s

must solve the q-BDHI problem.

Confidentiality: Because the U contains secret random
number and public of recipient, this information only
intended recipient can compute K and recover m. because
the K compute form v r and r included in U, therefore,
only the person that has the private key which is
correspond with identity used in a U, can computes a K,
K = H 2 (e(U, d IL)R )~). If the attacker can extract the r
form U then he can retrieve the plaintext. But he cannot
do that, only If he can find the value of r by calculating

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

the discrete logarithm of rP from r(^P 1 + q IDR P^. In our
scheme, the confidentiality is the same as the Sakai-
Kasahara, unsigncryption phase, anyone can not retrieve
the message without has the intended private key.

Verifiability: Anyone can verify the signature by step
5 of Unsigncryption, so our scheme provides the public
verifiability without need to secrete parameters.

Non-Repudiation: The scheme provides
nonrepudiation. the sender use his private key in
signcrypt, in order to prevent the sender attempts deny
the fact that she/he is signcrypted the message multiply
the private key of sender with x which is used to generate
the r, alse add the last with zP which is produce form
XOR between K and a, and z is necessary to retrieve the
message. Therefore; if recipient use the z to decrypt the
message and use it with public key of the sender and x to
verify the signature, then the sender cannot deny.

C. Efficiency

It now assesses the comparative efficiency of some
identity-based signcryption schemes, implemented
according to their original descriptions. Table- 1
summarizes the number of relevant basic operations
underlying several identity-based signcryption. It only
consider the pairing, point multiplication, exponentiation
computation, and hash

Reference

[1] A. Shamir, Identity Based Cryptosystems and
Signature Schemes, Advances in Cryptology -
Crypto' 84, LNCS 0196, Springer, 1984.

[2] AlexanderW. Dent • Yuliang Zheng, " Practical
Signcryption", springer, 2010.

[3] Sufyan T. Faraj Al-Janabi, Hussein Khalid Abd-
Alrazzaq, "Combining Mediated and Identity-Based
Cryptography for Securing E-Mail", Springer-
Verlag Berlin Heidelberg, 201 1.

[4] Y. Zheng, H. Imai, Efficient Signcryption Schemes
On Elliptic Curves, Proc. of IFIP/SEC98,
Chapman & Hall, 1998.

[5] J. Baek, R. Steinfeld, Y. Zheng, "Formal Proofs for
the Security of Signcryption", Proc. of PKC02,
LNCS 2274, Springer, pp. 81-98.

[6] Cheng, Zhaohui and Chen, Liqun (2005), "Security
proof of Sakai-Kasahara's identity-based
encryption scheme", 10th EVIA International
Conference on Cryptography and Coding,
Cirencester, UK, Springer-Verlag, pp. 442-459

[7] Martin, L.: Introduction to Identity-Based
Encryption. Artech House Inc. (2008).

[8] X. Boyen. Multipurpose identity-based
signcryption: A Swiss army knife for identity-
based cryptography). In D. Boneh, editor,

Table 1, The computation necessary for various IBSC

Scheme

Sign/Encrypt

Decrypt/Verify

Pairings

exp

mul

hashs

Pairings

exp

mul

hash

Boyen-IBSC [8]

Libert-Quisquater-IBSC [9]

Malone-Lee [10]

Chen-Malone-Lee [11]

Our scheme

VI. Conclusion

In this paper, it has proposed extended the Sakai-
Kasahara IBE scheme to build a new efficient identity
based signcryption scheme that provides a best security
than other scheme (such as Malone-Lee's scheme)
because it satisfies security under g-BDHI assumption
which is a stronger assumption than the difficulty of the
computational bilinear problem. In addition, this scheme
is derivation from Sakai-Kasahara full-scheme then it is
resistant to chosen plaintext attacks, adaptive chosen-
identity attacks, chosen-ciphertext attacks, and adaptive
chosen-identity attacks. This scheme is more efficient
than the approach consisting in combining the Sakai-
Kasahara encryption scheme with a signature. Our
scheme seems to be a reasonable base for the security of
cryptosystems comparative with the others.

Advances in Cryptology - Crypto 2003, volume
2729 of Lecture Notes in Computer Science, pages
383-399. Springer, 2003.

[9] Benoit Libert, Jean-Jacques Quisquater, "A new
identity based signcryption scheme from pairings",
ITW2003, Paris, France, 2003

[10] J. Malone-Lee. "Identity-based signcryption".
Cryptology ePrint Archive, Report 2002/098, 2002.
http://eprint.iacr.org/2002/098 .

[11]L. Chen and J. Malone-Lee. Improved identity-
based signcryption. In PKC'05, volume 3386 of
LNCS, pages 362(379. Springer, 2005.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Multi-Pixel Steganography

Dr. R. Sridevi

Department of Computer Science & Engineering

JNTUH College of Engineering

Hyderabad, A.P., INDIA

sridevirangu@yahoo.com

G. John Babu

Department of Computer Science & Engineering

Sreekavitha Engineering College

Khammam- A.P. - INDIA

johnbabug@gmail.com

Abstract — With the advent of digital images information hiding
in images known as Image steganography has gained wide
acceptance as a means covert communications. This paper
presents an innovative technique to hide the information in
images. For any steganographic technique the evaluating
parameters are deformation of cover image by the hidden
message and the amount of hidden message. The former
parameter should be minimum where as the payload or the size
of information that can be hidden should be maximum. The
proposed technique offers high payload with minimal distortion
of the cover image.

Keywords-component; Steganography,
Pixel value differencing, hiding capacity

Information hiding,

I. Introduction
Steganography is defined as the practice of undetectably
altering a Work to embed a secret message[l]. Information
hiding (or data hiding) is a general term encompassing a wide
range of problems beyond that of embedding messages in
content. The term hiding here can refer to either making the
information imperceptible (as in watermarking) or keeping the
existence of the information secret. The explosion of Internet
and multimedia content has paved the way for increasing
focus on the methods of digital steganography. The goal of
steganography is to convey a secret message under a cover
and concealing the very existence of information[2]. Any
method of steganography involves a medium to hide the secret
data referred to as cover medium. Cover media for
steganography includes, text, images, audio, video etc.
Among these images have been the best suitable medium for
the steganography, due to the redundancy in the digital
images. The original image in which the secret message is
referred to as cover image and the altered image in which a
secret data is embedded is called a stego-image. The
performance of a steganographic method can be evaluated by
the similarities between the cover image and stegoimage. An
efficient steganographic method produces a stego image that is
very similar to the cover image [3], The art of detecting the
existence of secret message is called steganalysis. Digital
image is an array of numbers that indicate light intensities at
various points called pixels [4]. A gray scale image consists of
pixels with intensity range [0,255]. Black is represented by
pixel value Oand white color is represented by pixel value 255.

Each pixel value can be represented by a 8 bit binary number.
These pixel values are manipulated or modified as per the
steganographic scheme to embed a secret message.

II. METHODS OF STEGANOGRAPHY

In this section we would like to review some of the existing
and related methods. A mostly widely used method is Least
Significant Bit method. (here onwards referred to as LSB
method) In this method the least significant bit(s) are replaced
by the secret message bits. And the receiver will arrange all
the least significant bit(s) of the binary values of the pixels to
get the secret message. Though LSB method is simple and
easy to implement it is very vulnerable to the lightest
modifications in stego image[4]. Many steganalysis methods
have been proposed to detect LSB replacement and it is even
possible to estimate the length of secret message hidden in
cover image[4][5][6]. Many modifications have been
proposed in the past decade for this LSB method to make it
more effective and efficient [7] [8]. Numerous Spatial domain
steganographic techniques have been proposed in the past
decade. Some of the related methods to the proposed method
in this paper are Wu and Tsai's pixel value differencing
method and Chang and Tseng's side match method.

III. Review of Related Methods

Wu and Tsai scheme[9] was a major breakthrough in spatial
domain techniques. This method has produced high quality
stego images when compared with LSB and other peer
methods. The cover images used in the PVD method are
supposed to be gray scale images. In the embedding phase a
difference value d is computed from every non-overlapping
block of two consecutive pixels, say p t and p i+ , of a given
cover image. The way of partitioning the cover image into
two-pixel blocks runs through all the rows of each image in a
zigzag manner. Assume that the gray values of p ; and pi+iare g,
and g i+ i, then d is computed as g,+y- g; which may be in the
range from -255 to 255. A block with d close to is
considered to be an extremely smooth block, whereas a block
with d close to -255 or 255 is considered as a sharply edged
block. The method only considers the absolute values of d (0
through 255) and classifies them into a number of contiguous
ranges, such as Rk where k=l,2,...,q. These ranges are
assigned indices 1 though n. The lower and upper bound

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

values of Rk are denoted by Ik and ulc, respectively. The width
of Rk is ufc-lk+l.In PVD method, the width of each range is
taken to be a power of 2. Every bit in the bit stream should be
embedded into the two-pixel blocks of the cover image. Given
a two-pixel block B with gray value difference d belonging to
kth range, then the number of bits, say n, which can be
embedded in this block, is calculated by n=log (uk-lk+1)
which is an integer. A sub-stream S with n bits is selected
from the secret message for embedding in B. A new
difference d' then is computed with equation 1.

;/ t+ * d>o (1)

■ r (l k +b)d<0

where b is the value of the sub-stream S . Because the value b
is in the range [0, u k -lj, the value of d' is in the range from Ik
to uk . If we replace d with d\ the resulting changes are
presumably unnoticeable to the observer. Then b can be
embedded by performing an inverse calculation from 'd' to
yield the new gray values (gi*, gi+1*) for the pixels in the
corresponding two-pixel block (Pi,Pi+i) of the stego-image.
The inverse calculation for computing (gi*, gi+1*) from the
original gray values (g ; , g i+1 ) of the pixel pair is based on a
function given in equation 2.

.. .. . (k-l^M'V^ di * Bdd

^* J "lCfl«-l m / 2 J^i- L [ m /2l)'/^ CTB n (2)

where m is \d' — d\ . The embedding is only done for pixels

which their new values would fall in the range of [0,255]. In
the extracting phase, the original range table is necessary. It is
used to partition the stego-image by the same method used for
the cover image. Calculate the difference value d*(p t , p i+ i) for
each block of two consecutive pixels Then, find the optimum
Ri of the d* same as in the hiding phase. Subtract lj which is
the lower bound of the range R ; from d*(p ( , p i+ i) and bo is
obtained. The bO value represents the secret data in decimal
number. Transform bO into binary with t bits, where t =
[log 2 wJ. The t bits can stand for the original secret data of
hiding. Existing steganalysis methods could not detect the data
embedding by PVD Method. The only disadvantage of this
method is that the embedding capacity reduces considerably
compared with LSB and other related methods.

A considerable improvement of PVD method has
been proposed with a title 'Pixel Value Differencing Method
with Modulus function'. This method has produced high
quality stego images than those produced by PVD method [10]
with same embedding capacity. This method adjusts the
modulus of the sum of two consecutive pixels to embed a
secret message instead of pixel value difference adjustment
used in PVD method. This method has shown considerable
improvement in stego image quality.

IV. Proposed Embedding Method

The major disadvantage of the PVD modulus method is that

the embedding capacity is moderate, when compared to LSB

and other methods. In the proposed method an attempt has

been made to enhance the embedding capacity within the

acceptable limits of quality for stego-images. In this method a

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
group of four pixels is considered as a block, as shown in the
figure below.

p 1

P (-l,v)

P (x,y + 1)

P {x* !,>■ + !)

Fig:l
The whole image is divided into the blocks of four group
pixels, i.e in a 512x512 image can be divided into 256 x 256
blocks. One of such blocks is shown in the above figure. The
top left pixel is taken as the reference pixel and the pixel value
difference between the three other pixels and the referenced
pixel is calculated.

d a =

"(x+V) ^W)

A range table has been used for embedding process. Range
table is table of different ranges with in 0-255. Each range
width is taken as some power of 2. And number of bits that
can be embedded is determined by the width of the range in
which the pixel value difference falls. The range table used in
the proposed method is shown in the figure 2.

width

No. of bits that
can be hidden

128

Fig 2.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

For each d; value suitable range is chosen from the range table
and the number of bits t ; that can be embedded in that pixel
pair is calculated from the width of the range IwJ. For each of
three blocks the secret data binary bits(t ; number of bits) are
converted to decimal equivalents v ; . For each pixel pair in the
block F rem is calculated from the following equation.

Fremffl = ( P (U) + P (i.y>) mod V i

By using the following criterion Compute the modified values
of the three pixel pairs

case 1:

£) > v and

T--

r ,;ua

case 2:

-'"-l

and

m<-

and P i: ^

P fi.«

m < — and P,;^ < P* y.

P C1jO _ *W) l Ilk P fly5 _ P ft.y) I W
case 3:

FremG) "* v

onri m > —

and P:; i3! ) > P :iv .,

P" — P ■

OwO two

+r/ 2 j; p c l„

= ^+rv 2 i

case 4:

p remCD > v

■ tl
ono m > —

and P (ijd < P ftj0

P (v0 = P [iJfl

+r/ 2 i; ^

= ^«+rv 2 j

case 5:

Fr&mM ^ v

and n: < -7-

and ?; Xid > P^

P" — P ■

+ 1- /2J ; P £i,fl

=%,«+r/ 2 i

case 6:

F rem(D ^ v and ™ ^ T" and P ii.x) < P fi.jn

p ,:uo = p a* + Im / 21: P C lrt = p aw + lm / 2J
case 7:

F r E rr..ij ^ *"' and K- > — and P ii.£' ^ P ;i.y1

P^ = P<^ - fm I / 21 ;P^ = P^ - lm L / 2j
case 8:

and

n: > — - and P

u.ii> < P "i

-w

P .U = p ^ - l»i / 2 J ; p d« = p m ~ K / 2 1

where n: = iF^^.-j-, —v." and n: ; = 2 1 ' — |F ren , fil

From the above criterion new values for the four pixel block
are calculated. And from among the three pairs present in the
block, the pair which produces minimum Mean Square
Error(MSE) is chosen as the reference pair. Keeping the
values of the reference pair as constant, other two pixels
values are adjusted with offset. The total number of bits
embedded is t!+ t 2 +t 3 .

V. PROPOSED EXTRACTION METHOD

The extraction method is quite simple and the the range table
used in the embedding process is supposed to be available in
extraction process. The stego-image is divided into blocks of
four pixels, same as in the embedding process. For each pair
the difference dj is calculated. And for each dj the suitable
range is identified and the number of bits embedded is
computed(ti=log 2 lwl). F rem is calculated for each pair. That
value of F lem is the decimal equivalent of the binary bits
embedded.

VI. RESULTS & ANALYSIS

To demonstrate the accomplished performance of proposed
approach in capacity of hiding secret data in the stego-image,
and Stego-image quality, I have conducted different

experiments using twelve images Baboon, Boat, Couple,
Elaine, Jesse, Jet, Leena, Man, Peppers, Tank, Tiffany, and
Truck, with range widths 8,8,16,32,64,128 in the range table.
The proposed method is proved to be facilitating higher
embedding capacity than the PVD modulus method. The
results are shown in the tablel.

Capacity in Bytes

image

PVDM

Proposed

%increase

Baboon

57162

89315

56.25

Boat

52428

78732

50.17

Couple

51604

78431

52.00

Elaine

50021

75589

51.11

Jesse

55283

82627

49.50

Jet

51301

77817

51.70

Lena

51233

76762

49.83

Man

53940

81268

50.66

Peppers

50922

76647

50.52

Tank

50111

76104

51.87

Tiffany

50821

76203

50.00

Truck

49704

76194

53.30

Tablel : Hiding Capacity

PSNR VALUES

PVD

PVDM

Proposed

Baboon

36.86

40.19

32.82

Boat

38.97

42.086

36.48

Couple

40.259

43.471

36.43

Elaine

42.737

45.494

39.51

Jesse

36.973

40.201

34.21

Jet

40.26

43.406

36.59

Lena

41.09

44.017

38.05

Man

36.973

41.162

35.05

Peppers

40.693

43.552

37.30

Tank

42.752

45.498

39.32

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Tiffany

40.861

44.149

37.84

Truck

43.226

45.908

39.55

Table 2: PSNR values
Signal-to-noise (SNR) measures are estimates of the
quality of a reconstructed image compared with an original
image. The basic idea is to compute a single number that
reflects the quality of the reconstructed image. Reconstructed
images with higher metrics are judged better. In fact,
traditional SNR measures do not equate with human subjective
perception. Signal-to-noise measures are easier to compute.
We have to note that higher measures do not always mean
better quality.

The actual metric we will compute is the peak signal-to-
reconstructed image measure which is called PSNR. Assume
we are given a source image f(i,j) that contains N x N pixels
and a reconstructed image F(i,j) where F is reconstructed by
decoding the encoded version of f(i,j). Error metrics are
computed on the luminance signal only so the pixel values
f(i,j) range between black (0) and white (255).
First we compute the mean squared error (MSE) of the
reconstructed image as follows

s:pa..jj - F(i,ji] :

MSE = — .

N-

The summation is over all pixels. The root mean squared error
(RMSE) is the square root of MSE. Some formulations use N
rather N A 2 in the denominator for MSE.
PSNR in decibels (dB) is computed by using

PSNR = 10 log :

255
MSE

Typical PSNR values range between 20 and 40. The actual
value is not meaningful, but the comparison between two
values for different reconstructed images gives one measure of
quality. The PSNR values of the twelve test images with the
proposed method are calculated. These values are compared
with the PSNR values with Pixel Value Differencing Method,
Pixel Value Differencing with Modulus function method
Triway Pixel Value Differencing method . The results have
been shown in table 2.

From the these results it can be inferred that the proposed
method produces stego-images with acceptable PSNR.
No reliable steganalytic technique has been proposed in the
literature for PVD based methods. The RS steganalysis
method is not effective on PVD based methods as that method
was devised for LSB steganography[3].

The stego- images are shown in the figure 3.

i?V~

Sb-

■'0

" % >

gbgk^igX

■Mf

m w i«.ii

I'll/

\ I / K 1* /

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

PVDM method

Proposed method

Figure 3 : Stego- images

VII. STEGANALYSIS

It has been already proved that Steganalysis methods such as
RS-steganalysis cannot detect steganography using PVD with
modulus function. No other methods are suitable to detect this
technique.

VIII. CONCLUSION

It is proved by the experimental results that the proposed
method offers high capacity than the Pixel Value Differencing
method(PVD), Pixel Value Differencing with modulus
function method(PVDM). PSNR values for the stego images
obtained by the proposed method are well above the
acceptable limit of 20-35. Thus it can be concluded that the
proposed method is a method which offers high pay load with
acceptable quality of Stego images.

REFERENCES

[1] Ingemar J. Cox, Matthew L. Miller, Jeffrey A. Bloom Jessica Fridrich

and Ton Kalker, " Digital Watermarking and Steganography" 2 nd

Edition, Morgan Kaufmann Publishers, 2008.
[2] Huaiqing wang and shuozhong wang, "Cyber Warfare:Steganography

vs. Steganalysis" communications of the ACM , Vol. 47, 2004, pp. 76-

82
[3] Chung-Ming Wang, Nan-I Wu, Chwei-Shyong Tsai, and Min-Shiang

Hwang, "A high quality steganographic method with pixel-value

differencing and modulus function" The Journal of Systems and

Software, vol.81, 2008, pp.150-158
[4] Johnson, N. and Jajodia, S. "Exploring steganography: Seeing the

unseen", IEEE Computers. Vol.31, 1998, pp.26-34.
[5] C.-K. Chan, and L.M. Cheng , "Hiding data in images by simple LSB

substitution" Pattern Recognition, vol.37, 2004, pp. 469 — 474

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

[6] Fridrich, J., Goljan, M., Du, R., "Detecting LSB steganography in color

and gray-scale images". Mag. IEEE Multimedia (Special Issue on

Security) ,2001, pp. 22-28.
[7] C.C. Chang, J.Y. Hsiao, and C.S. Chan, "Finding optimal LSB

substitution in image hiding by dynamic programming strategy",

Pattern Recognition, vol.36, 2003, pp.1583-1595.
[8] R.Z. Wang, C.F. Lin, and J.C. Lin, "Image hiding by optimal LSB

substitution and genetic algorithm", Pattern Recognition, vol. 34,

2001, pp.671-683.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
[9] Da-Chun Wu, and Wen-Hsiang Tsai, "A steganographic method for
images by pixel-value differencing" Pattern Recognition Letters vol.
24, 2003,pp.l613-1626.
[10] Chung-Ming Wang, Nan-I Wu, Chwei-Shyong Tsai, and Min-

Shiang Hwang, "A high quality steganographic method with pixel-
value differencing and modulus function", The Journal of Systems and
Software, vol. 81,2008, pp.150-158

AUTHORS PROFILE

Dr. Sridevi Rangu obtained B.E (Computer Science and
Engineering) from Madras University, chennai, and M.Tech
(Computer Science and Technology) from Andhra
University Visakapatnam in 1999 and 2003 respectively.
She is having nearly 12 years of teaching experience. Since
November,2006 she is working as an Associate professor
in JNTU Hyderabad. She pursued Ph.D. from faculty of
Computer Science and Engineering JNTU Hyderabad in
December,2010. Area of research interest are Network
security, Intrusion Detection and Computer Networks.
She is Guiding 6 Ph.D students in the area of network
security and guided more than 25 M.tech
students. Published 7 research papers in various Internal
Journals and conferences. She achieved best Paper Award
in ICSCI-2008, Pentagram Research Center , Hyderabad
,India,2008.

Mr. G. John Babu, has completed his masters degree in
computer science from JNTUH Hyderabad, and currently
doing research on Steganalysis. His research interests
include Steganography, Steganalysis and watermarking.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Design of 16 bit low power processor

Khaja Mujeebuddin Quadry

Member IEEE,

Professor, Department of ECE

Royal Institute of Technology & Science,

Chevella, R. R. Dist. A. P. India

Email: mujeebqd(a)vahoo.com

Dr. Syed Abdul Sattar,

Professor & Head, Department of ECE

Royal Institute of Technology & Science,

Chevella, R. R. Dist. A. P. India
Email: syedabdulsattarl965@gmail.com

Abstract — This paper describes the design of low power 16 bit
processor. The processor is designed to incorporate 25 basic
instructions involving Arithmetic, Logical, Data transfer,
Branching, Control instructions. It is expandable up to 32
instructions based on the user requirements To implement these
instructions the design incorporates various design blocks like
Control Logic Unit (CLU), Arithmetic Logic Unit (ALU),
program Counter (PC), instruction register (IR), Memory ,
Clock, Generator, Register and Additional glue logic. The
processor has been realized using Verilog HDL, functionality is
verified by writing the test programs using XILINX 9i ISE.
Power estimation is done using X power tool, synthesis is done for
SPARTAN 2E, SPARTAN 3E, and VIRTEX 5 FPGA.
Comparison of synthesis results for various FPGA technologies
has been carried out. The simulations results depict that total
dissipated power by the processor to be approximately varying
from 25mW to 267mW with the maximum frequency of
operation ranging 30.931MHz to 122.018MHz. The bit stream file
generated is successfully generated loaded in to SPARTAN 3E
FPGA and tested the results using chip scope pro tool.

Keywords-16 bit
insert

processor; FPGA; lopower design; styling;

Introduction

The reduced instructions set computer (RISC) use simple
instructions and have small instructions set compared to
complex instruction set computer (CISC). It has become a
mainstream movement to improve computing power and keep
cost of design time low by using RISC processor. It is
basically designed in order to achieve faster executions. The
striking feature of RISC is that it executes instructions in short
clock cycles [1]. Almost all instructions have simple register
addressing. Due to the simplification of the instructions and
their format control logic designed is very much simplified. As
power has become an important aspect in the design of general
purpose processors. Low power consumption helps to reduce
the heat dissipation, lengthen battery life and increase device
reliability [2]. The minimization of power dissipation is done
at various levels of design process by applying various low
power techniques.

II. DESIGN OF 16-BIT RISC CPU

The overall architecture of a RISC Processor consists of three
functional units: a processor, a controller, and memory as
shown in Figure 1 program instructions and data are stored in
memory. Instructions are fetched synchronously from
memory, decoded, and executed .The instruction register
contains the instruction that is currently being executed; the
program counter contains the address of the next instruction to
be executed; and the address register holds the address of the
memory location that will be addressed next by a read or write
operation.

controller
ins true tu>n.

Load_R0
Load_Kl

Load_K2
Load_R3

Loai_R4

Load_K£
Load R£

Load_PC
Inc PC

opcode

Load_Reg c
Load_Reg z

7 6 5 4 3 2 10

\r*£* I S JReS-C

Figure 1. RISC Processor architecture

A. RISC Processor

The processor includes registers, data-paths, control
lines, and an ALU capable of performing arithmetic and logic
operations on its operands, subject to the op-code held in the

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

instruction register. A multiplexer Mux_l determines the
source of data that is bound for Bus_l, and Mux_2 determines
the source of data bound for Bus_2, and loaded into the
instruction register. A word of data can be fetched from
memory, and steered to a general-purpose register or to the
operand register (Reg_Y) prior to an operation of the ALU.
The result of an ALU operation can be placed on Bus_2,
loaded into a register, and subsequently transferred to
memory. A dedicated registers (Reg_Z, Reg_c) holds the
flags indicating zero and carry generated respectively after an
ALU operation .

B. RISC Controller

The timing of all activity is determined by the controller. The
controller steers the data to the proper destination, according
to the instruction being executed. Thus, the design of the
controller is strongly dependent on the specification of the
machine's ALU and data path resources and the clocking
scheme available. In this a single clock is used, and execution
of an instruction is initiated on a single edge of the clock (i.e.
the rising edge). The controller monitors the state of the
processing unit and the instruction to be executed and
determines the value of the control signals. The controller's
input signals are the instruction word and the zero, carry flags
from the ALU. The signals produced by the controller are
identified as follows.
1) Controller signals

Load-Add-Reg -Loads the address register,

Load-PC- Loads Bus_2 to the program counter,

Load-IR- Loads Bus_2 to the instruction register

Inc-PC- Increments the program counter,

Set_Bus_l_Mux-Selects among the Program-Counter,
R0toR6 to drive Bus_l,

Set_Bus_2_Mux- Selects among ALU-out, Bus_l, and
memory to drive Bus_2,

Load_R0 -Loads general-purpose register RO,

Load_Rl -Loads general-purpose register Rl,

Load_i?2-Loads general-purpose register R2,

Load_i?3-Loads general-purpose register R3,

Load_R4 -Loads general-purpose register R4,

Load_i?5-Loads general-purpose register R5,

Load_i?6-Loads general-purpose register R6,

Load_i?eg_Y-Loads Bus_2 to the register Reg_Y,

Load_i?eg_Z-Loads the register Reg_Z,

Load_Reg_C Loads the register Reg_C,

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
• Wr/te-Loads Bus_l into the SRAM memory at the
location specified by the address register. The control
unit produces the control signals to load registers,
selects the path of data through the multiplexers,
determines when data should be written to memory,
and controls the three-state busses in the architecture.

C. RISC Processor Instruction set

The machine is controlled by a machine language
program consisting of a set of instructions stored in memory.
So in addition to depending on the machine's architecture, the
design of the controller depends on the processor's
instructions set ( i.e., the instructions that can be executed by a
program ). A machine language program consists of a stored
sequence of 16-bit words (2 bytes). The format of an
instruction of RISC Processor can be long or short, depending
on the operation.

opcode

source

Destination

Figure 2. Instruction format Short Instruction

opcode

source

Destination

Address

Figure 3. Instruction format Long Instruction

Short instructions have the format shown in Figure 2. Each
short instruction requires 2 bytes of memory. The word has a
5-bit op-code, a 3-bit source register address, and a 3-bit
destination register address. A long instruction requires 4
bytes of memory. The first word of a long instruction contains
a 5-bit op-code. The remaining 6 bits of the word can be used
to specify address of a pair of source and destination registers,
depending on the instruction. The second word contains the
address of the memory word that holds an operand required by
the instruction. Figure 3. shows the 4-byte format of a long
instruction. The program counter holds the address of the next
instruction to be executed. When the external reset is asserted,
the program counter is loaded with 0, indicating that the
bottom of memory holds the next instruction that will be
fetched. Under the action of clock, for single-cycle
instructions, the instruction at the address in the program

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

counter is loaded into the instruction register and the program
counter is incremented. An instruction decoder determines the
resulting action on the data paths and the ALU. A long
instruction is held in 4 bytes, and an additional clock cycle is
required to execute the instruction. In the second cycle of
execution, the second byte is fetched from memory at the
address held in the program counter, and then the instruction is
completed. Intermediate contents of the ALU may be
meaningless when two-cycle operations are being executed.
The RISC Processor instruction set is summarized in TABLE I

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
III. RISC PROCESSOR CONTROLLER DESIGN

TABLE I.

INSTRUCTION SET TABLE

Instruction

Instruction Word

Action

opcode

src

dest

NOP

00000

None

ADD

00001

src

dest

dest <= src +dest

SUB

00010

src

dest

dest <= src -dest

AND

00011

src

dest

dest <= src &dest

NOT

00100

src

dest

dest <= ~src

00101

dest

dest<= memory [Add_R]

00110

src

memory [Add_R]<=src

JMP

00111

PC<= memory [Add_R]

01000

PC<= memory [Add_R]

01001

src

dest

dest <= src | dest

XOR

01010

src

dest

dest <= src A dest

INC

01011

src

dest

dest <= src+1

DEC

01100

src

dest

dest <= src-1

MUL

01101

src

dest

dest <= src *dest

SHL

OHIO

src

dest

dest <= src <<1

SHR

01111

src

dest

dest <= src>>l

ROL

10000

src

dest

dest<= rotate left src by 1
bit

ROR

10001

src

dest

dest<= rotate right src by
lbit

ADC

10010

src

dest

dest <= src +dest+carry

SBC

10011

src

dest

dest <= src - dest - carry

RLC

10100

src

dest

dest<= rotate left src by
1 bit through carry

RRC

10101

src

dest

dest<= rotate right src by
1 bit through carry

10110

src

dest

dest<= port[Add_R]

OUT

10111

src

dest

port[Add_R]<=src

HALT

11111

Halts execution until
reset

The machine's controller is designed as an FSM. Its states are
specified, according to the architecture, instruction set, and
clocking scheme used in the design. This is accomplished by
identifying what steps must occur to execute each instruction.
We have used an ASM chart to describe the activity within the
Processor, and to present a clear picture of how the machine
operates under the command of its instructions.

The machine has three phases of operation: fetch
decode, and execute as shown in Figure4. Fetching retrieves
an instruction from memory, decoding decodes the instruction,
manipulates data paths, and loads registers; execution
generates the results of the instruction. The fetch phase will
require two clock cycles - one to load the address register and
one to retrieve the addressed contents from memory. The
decode phase is accomplished in one cycle.

The execution phase may require zero, one, or two more
cycles, depending on the instruction. The NOT instruction can
execute in the same cycle that the instruction is decoded;
single-word instructions, such as ADD, take one cycle to
execute, during which the results of the operation are loaded
into the destination register. The source register can be loaded
during the decode phase. The execution phase of 2 word
instruction will take one cycle to load the address register with
the second word, and one to retrieve the word from the
memory location addressed by states listed below, with the
control actions that must occur in each state.

A. Controller states

• S_idle: State entered after reset is asserted and no
action takes place, The FETCH state is further divided
in to two more states S_ fetl and S_fet2.

Figure 4. State machine of Controller

S_ fetl:Load the address register with the contents of
the program Counter. (PC is initialized to the starting
address by the reset action). The state is entered at the

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

first active clock after reset is de-asserted, and is
revisited after a NOP instruction is decoded.

• S_fet2: Load the instruction register with thw word
addressed by the Address register, and increment the
program counter to point To the next location in
memory, in anticipation of the next Instruction or data
fetch.

• S_dec: Decode the instruction register and assert
signals to control Data paths and register transfers.

The Execute state is divided in to S_exl, S_rdl, S_rd2, S_wrl,
S_wr2, S_brl, S_br2, SJialt.

• S_exl: Execute the ALU operation for a single-byte
instruction, Conditionally assert the zero flag, carry
flag and load the destination Register.

• S_rdl: Load the address register with the second byte
of a RD instruction, and increment the PC.

• S_rd2: Load the destination register with the memory
word addressed by the byte loaded in S_rdl.

• S_wrl: Load the address register with the second byte
of a WR instruction, and increment the PC.

• S_wr2: Load the destination register with the memory
word addressed by the byte loaded in S_wrl

• S_brl: Load the address the register with the second
byte of a BR instruction and increment the PC.

• S_br2: Load the program the counter with the memory
word addressed by the byte loaded in S_brl

• S_halt : default state to trap failure to decode a valid
instruction.

The partitioned ASM chart for the controller of RISC
Processor have been built, entire machine is described using
verilog HDL, for the given architectural partition. This process
has been done in stages. First, the functional units are declared
according to the partition of the machine. Then their ports and
variables are declared and checked for syntax. Then the
individual units are described, debugged, and verified. The last
step is to integrate the design and verify that it has correct
functionality. The top level verilog HDL module
RISC_Processor_16_bit integrates the modules of the
architecture of Figure 1 and were presented first. Three
modules are instantiated; processing-unit, control unit and
Memory-unit, with instant names MO-processor, Ml-
Controller, and M2-Mem respectively. The parameters
declared at this level of the hierarchy size the data paths
between the structural/functional units. The verilog model of
the machine processor is describe the architecture; register
operations that are represented by the functional units shown
in Figure 1 The processor instantiates several other modules
which are also declared the simulation results can be seen in
the Figure 5.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
IV. RESULTS & CONCLUSION

The design of a 16-Bit non-pipelined RISC processor has been
presented. The processor has been designed for executing the
instruction set comprising of 25 instructions in total. It is
shown that it can be expandable up to 32 instructions, based
on the user requirements. A memory unit with 16 bit word size
and 256 locations also designed and integrated with the
processor to store the machine code of the program to be
executed.

Figure 5. Simulation result

The RISC processor for embedded and portable application
has been designed and verified. The low power techniques
employed are reducing the supply voltage, clock gating, and
resource sharing. Frequency of operation is also selected
according to the timing report and power budget. Gray code is
used for state encoding as it consumes less power compare to
binary coding.

Figure 6. Floor plan for VIRTEX 5

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

The designed processor executing memory read, write, branch
instructions in 5 clock cycles (fetch, decode, execute),
arithmetic and logical instructions are executed in 3 to 4 clock
cycles. Synthesis report for the target device xc5vlx30-3-ff324
(VIRTEX 5) shows that the design has 9 Levels of Logic with
a delay of 8.196ns corresponds to a maximum frequency on
operation 122.018MHz, and for a target device xc2s50e-6-
ft256 (SPARTAN HE) the delay is 32.330ns corresponds to
Maximum Frequency: 30.931MHz.Post map static timing
analysis is performed by assigning the user constraints and
verified the timing constraints met. The floor plan of the
design for VIRTEX 5 is shown in the Figure 6. Bit stream file
is generated for SPARTAN 3E xc3s500e-5 device with FG320
package and successfully loaded and contents of the memory
are verified using chipscope-pro after executing the
application programs it is observed that the power saving is
about 21% after applying the low power techniques.

Acknowledgment

I would like to thank Dr. K. Soundara Rajan, Professor Dept
of ECE, JNTU Anantapur, A.P.India for the time to time
discussions

References

[1] M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E.
Berger, R. Wheeler, and A. Ng, "Ros: an open-source robot operating
system," in Proc. of the IEEE Intl. Conf. on Robotics and Automation
(ICRA) Workshop on Open Source Robotics, (Kobe,Japan), May 2009.

[2] Nidhi Maheshwari, Pramod Kumar Jain, D.S. Ajnar "A 16-Bit Fully
Functional Single Cycle Processor" Proc. International Journal of
Engineering Science and Technology (IJEST) ISSN : 0975-5462 Vol. 3
No. 8 August 2011 p 6219-6226

[3] Samiappa Sakthikumaran, S. Salivahanan, V. S. Kanchana Bhaaskaran
"16-Bit RISC Processor Design for Convolution Application"
proceedings of IEEE-International Conference on Recent Trends in
Information Technology, ICRTIT 2011 978-1-4577-0590-8

[4] Youngjoon Shin, Chanho Lee, and Yong Moon, "A Low Power 16-Bit
RISC Microprocessor Using ECRL Circuits", ETRI Journal, Volume 26,
Number 6, December 2004.

[5] Yasuhiro Takahashi, Toshikazu Sekine, and Michio Yokoyama, "Design
of a 16-bit Non-pipelined RISC CPU in a Two Phase Drive Adiabatic
Dynamic CMOS Logic," International Journal of Computer and
Electrical Engineering, Vol. 1, No. 1, April 2009 1793-8198.

AUTHORS PROFILE

Dr.Syed Abdul Sattar received M.Tech(DSCE) and PhD from JNTU
Hyderbad India. He has published many papers in

International/National journals. He is felleow of Institution of
Electronics and Telecommunication Engineers India Life member of
Indian society for Technical Education.

Khaja Mujeebuddin Quadry (Member IEEE), Received Diploma in
Electronics and communication engineering from state board of
Technical Education A.P India in 1993, BE Degree in Electroics and
Communication Engineering from Osmania University in 1997, ME
Degree in VLSI & Embedded system Design from Osmania University
in 2007. He is a Life member of Institution of Electronics and
Telecommunication Engineers India.

71 http://sites.google.com/site/ijcsis/

ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Integration Of Floating Point Arithmetic User
Library To Resource Library Of The CAD Tool

For Customization

R.Prakash Rao,
Associate professor,
St.Peter's Engineering College,
Maisammaguda,Hyderabad, India,
rachuri vlsi @ gmail.com.

Abstract - Towards the integration of analog and digital
circuitry, various approaches have been emerged. To
achieve better integrity, mixed-signal designs have recently
attain the greater significance. In various real time
applications such as RF Systems, Communication Systems,
Networking Systems etc., mixed signal integrated circuits
are emerging. Because the use of both digital system and
analog system on a single platform, the approachability is
quite complex. Where digital systems are complex with
synchronization problem, design of analog circuitry is too
typical. Due to various CAD tools usage, the bottleneck of
integration is also challenging. Hence, here our aim is to
develop a generic modeling of analog-digital mixed signal
design tool for easy to handle and low complex in approach.
Hence, here our work focused on integrating multiple
features of required analog design parameters to the
digitally defined CAD tool. It is also focused to have a user
friendly tool with various optimizing possibilities for easy
designing and testing purpose.

Keywords-RF Systems; mixed signal designs; analog circuitry;
integration; CAD tool; testing.

I. Introduction

In the past, designers have used a variety of simulation
methodologies to verify designs that contained both
analog and digital circuits. At the very highest levels of
abstraction, system designers have used C/C++ and
Matlab to model systems that would be implemented with
analog and digital circuits; but this approach usually
doesn't try to represent any implementation issues. At the
next level down in the hierarchy, designers have used
Saber by Analogy and similar tools to model mixed-signal
systems. At the lowest level of abstraction, designers have
modeled all the analog and digital circuits at the transistor
level and used Spice-like simulators, or reduced-
complexity transistor-level simulators.

Designers are just beginning to use the VHDL and
Verilog AMS languages, and this approach fits
somewhere in the middle compared to the above levels.
The AMS extensions allow a designer to use VHDL or
Verilog to describe analog circuits at different levels of

Dr.B.K.Madhavi, Professor,
Geetanjali College of Engineering
& Technolog, Cheryala,
Hyderabad, India.
bkmadhavi2009@gmail.com.

abstraction, ranging from behavioral to structural. The
AMS description is usually then translated to a netlist and
simulated with a Spice-like simulator.

Another approach offered by major CAD companies
is to provide a simulation environment that allows the
user to choose from different levels of abstraction for a
given simulation. Digital blocks are represented with an
HDL and simulated with an HDL simulator, analog
blocks are represented with transistors or an AMS HDL
and simulated with a Spice-like simulator. A software
backplane allows the HDL and Spice simulators to
communicate via interprocess communication. Typically
lower levels of abstraction translate to slower simulation
time. Consequently, simulating large mixed-signal
designs solely at the transistor level with a standard
Spice-like simulator may not be practical. The benefits of
Spice simulation tools are that they provide the most
detailed level of modeling and analysis including: DC,
transient, small signal AC, and zero's/pole's of filters .
The costs of Spice simulation are often long simulation
times and tedious design entry.

II. Proposed Design

Traditionally, if a system consists of both analog and
digital systems on the same platform called mixed-signal
systems, neither the analog tools nor the digital tools will
support mixed signal designs. These mixed signal designs
are used most-widely in DSP systems for audio and video
purposes. So, to simulate such mixed signal designs,
presently the dedicated floating point arithmetic units are
used in the CAD tools like XILINX new version ISE tool
[10] with the latest target devices like Vertex, Kintex etc.
These dedicated floating point arithmetic units need to
buy from different vendors. But, here we are proposing
that after the extensive study of the ModelSim tool flow,
instead of buying the dedicated floating pint arithmetic
units from different vendors , we have integrated the
floating point arithmetic features to the ModelSim tool in

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

which earlier we would have simulated the fixed point
numbers only. So that now the upgraded ModelSim tool
can be used to perform the complete DSP operations [4]
like video and audio.

Since, the analog signal is decomposed or
reconstructed with the signal samples and those signal
samples could be defined with floating point
arithmetic[l] like 0.0,0.2,0.4,0.6,0.8,1.0 and

1.0,0.8,0.6,0.4,0.2,0.0, as a continuous wave signal shown
in fig.l, these floating point features of analog signals had
been added to the resource library of the CAD tool,
hence, the particular tool will be upgraded with both the
analog and digital features .

- 1.0 PEAK

.707 RMS

Figl: Continuous wave signal

So, here we have designed the floating point adder,
multiplier, deviser, square root functions and integrated
to the ModelSim tool. In DSP, some algorithms like
Lifting scheme algorithm or Daubechy's algorithm [6] [9]
will produce the floating point co-efficients while
analyzing coder or decoder operations used for audio or
video. Hence such systems could be designed using the
upgraded ModelSim tool, hence it is more economical
comparing with dedicated floating point arithmetic units.

numbers are divided. Floating-point, on the other hand,
employs a sort of "sliding window" of precision
appropriate to the scale of the number. This allows it to
represent numbers from 1,000,000,000,000 to
0.0000000000000001 with ease.

A. IEEE 754 Floating Point Standard

IEEE 754 floating point standard is the most
common representation today for real numbers on
computers. The IEEE (Institute of Electrical and
Electronics Engineers) has produced a Standard to define
floating-point representation and arithmetic. The standard
brought out by the IEEE come to be known as IEEE
754[5]. The IEEE Standard for Binary Floating-Point
Arithmetic (IEEE 754) is the most widely used standard
for floating point computation, and is followed by many
CPU and FPU implementations. [2]

The standard defines formats for representing floating-
point numbers including negative numbers and denormal
numbers special values i.e. infinities and NANs together
with a set of floating-point operations that operate on
these values. It also specifies four rounding modes which
are round to zero, round to nearest, round to infinity and
round to even and five exceptions including when the
exceptions occur, and what happens when they do occur.
Dealing with fixed-point arithmetic will limit the usability
of a processor. If operations on numbers with fractions
(e.g. 10.2445), very small numbers (e.g. 0.000004), or
very large numbers (e.g. 42.243x105) are required, then a
different one representation is in order is the floating-
point arithmetic [4],

IILFloating Point Arithmetic

IV. Upgrading the cad Tool

There are several ways to represent real numbers on
computers. Fixed point places a radix point somewhere
in the middle of the digits, and is equivalent to using
integers that represent portions of some unit. For
example, one might represent l/100ths of a unit; if you
have four decimal digits, you could represent 10.82, or
00.01. Another approach is to use rational, and represent
every number as the ratio of two integers; Floating-point
representation - the most common solution - basically
represents reals in scientific notation. Scientific notation
represents numbers as a base number and an exponent.

For example, 123.456 could be represented as
1.23456 x 10 2 . In hexadecimal, the number 123. abc might
be represented as 1.23abc x 16 2 . Floating-point solves a
number of representation problems. Fixed-point has a
fixed window of representation, which limits it from
representing very large or very small numbers. Also,
fixed-point is prone to a loss of precision when two large

A. General

Since, ModelSim is the user friendly tool, in our
work we have chosen ModelSim tool, and upgraded the
features. ModelSim is a verification and simulation tool
for VHDL, Verilog, SystemVerilog, SystemC, and mixed-
language designs. Hence, here we are going to upgrade
the ModelSim simulation environment[12].

B. Simulation flow in ModelSim

The below fig 4.1 shows the basic steps for simulating a

design in ModelSim.

1. Creating the working library

In ModelSim, all designs are compiled into a library.
We start a new simulation in ModelSim by creating a
working library called "work". "Work" is the library name
used by the compiler as the default destination for
compiled design units.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

C reate a Working Library

Compile Design Files

[lehiHj Results

Figure 4.1: Basic Simulation Flow Diagram

2. Compiling the design

Before we simulate a design, we must first create a
library and compile the source code into that library as
given below.

i) Create a new directory and copy the design files for this
lesson into it. Start by creating a new directory for this
exercise,
ii) Start ModelSim if necessary.

• Use the ModelSim icon in Windows. Upon opening
ModelSim for the first time, we will see the Welcome
to ModelSim dialog. Click Close.

• Select File > Change Directory and change to the
directory you created in step (i).

iii) Create the working library.

• Select File > New > Library. This opens a dialog
where you specify physical and logical names for the
library. We can create a new library or map to an existing
library. We will be doing the former.

• Type work in the Library Name field if it is not
entered automatically Click OK.

ModelSim creates a directory called work and writes
a specially formatted file named_m/o into that directory.
The _info file must remain in the directory to distinguish
it as a ModelSim library. Do not edit the folder contents
from your operating system; all changes should be made
from within ModelSim. ModelSim also adds the library to
the list in the Workspace and records the library mapping
for future reference in the ModelSim initialization file
(modelsim.ini). When you pressed OK in above step,
several lines were printed to the Main window Transcript
pane:
vlib work
vmap work work

# Copying C:\modeltech\win32/. ./modelsim.ini to
modelsim.ini

# Modifying modelsim.ini

# ** Warning: Copied
C:\modeltech\win32/. ./modelsim.ini to
modelsim.ini.

# Updated modelsim.ini.

The first two lines are the command-line equivalent
of the menu commands you invoked. Many menu-driven
functions will echo their command-line equivalents in this
fashion. The other lines notify you that the mapping has
been recorded in a local ModelSim initialization file.
After creating the working library, compile the design
units into it. The ModelSim library format is compatible
across all supported platforms. We can simulate your
design on any platform without having to recompile your
design. With the working library created, we are ready to
compile your source files. We can compile by using the
menus and dialogs of the graphic interface, as in the
Verilog or VHDL.

V.Design Algorithms

a ) Addition

We now present the basic description of the floating-point
addition which gives a general idea of how it can be
performed.

• If any of the input operand is a special value, the
result is known before hand, thus all the
computation can be bypassed.

• Mantissas are added or subtracted depending on
the effective operation:

S =(mx ± (my x 2ey-ex) x 2ex if ex _ ey,
((mx x 2ex~ey ) ± my) x 2ey if ex < ey.

• In order for the addition or subtraction to take
place the binary point must be aligned. To
achieve this, the mantissa of the smaller operand
is multiplied by 2 difference of the exponents .
This process is called alignment.

• The exponent of the result is the maximum of ex and
ey; ez = max(ex, ey).

• If eop is addition, a carry-out can be generated and if
eop is subtraction, cancellation might Occur. In each
case normalization is required and consequently the
exponent ez is updated.

• The exact result S is rounded to fit in the target
precision. Sometimes rounding causes an
overflow of the mantissa; a post-normalization is
required.

• Determine exceptions by verifying the exponent of
the result.

b) Multiplication

We now present the basic description of floating-
point multiplication 11]

• Verification of special values: if any of the input
operandnis a special value, the result is known
beforehand and thus no computation is required.

• Mantissas are multiplied to compute the product as
P = mx x my.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

• The exponent of the result is computed as ez = ex +
ey - 127. In biased representation, the bias 127 (for
single precision) has to be subtracted.

• The sign of the result is computed as sz = sx XOR
sy.

• The product might be unnormalized. In this case,
normalization is required and consequently the
exponent ez is updated.

• The exact result P is rounded according to the
specified mode to produce the mantissa of the result
mz. In case of post-normalization, it is necessary to
increment the result exponent as ez = ez + 1 .

• Determine exceptions by verifying the exponent of
the result. When overflow occurs, the result is +1
and when underflow occurs, the result is zero or a
subnormal number (if the gradual underflow is
supported in the implementation).

c) Division

The basic description of floating-point division[3]
can be given as follows:

• Mantissas are divided and the quotient Q = mx/my
is computed.

• The intermediate result exponent is computed as

ez = ex - ey + B.

• The sign of the result is computed as

sz = sx XOR sy.

• If mx < my, normalization is required and
consequently the result exponent is updated as ez =
ez - 1.

• The exact quotient Q is rounded to fit in the target
precision. In case of division, rounding never
causes an overflow of the mantissa; thus post-
normalization is not required.

• Exceptions are determined by verifying the
exponent of the result.

d) Square root

The basic description of floating-point square root[3] can
be given as follows.

• If the unbiased exponent ex - 127 is odd, a new
mantissa is formed as mx, 2mx. This allows to
have an integer result exponent.

• Obtain the square root as T =pmx.

• The intermediate result exponent is computed as
ez = b(ex + B)/2c.

• The sign of the result is sz = 0. No normalization
is required .

• Round T according to the rounding mode. A
post-normalization step might be required.
Neither underflow nor overflow can occur .

vi. Results

a) Adder

The following snapshot shown in fig 6.1 had been taken
from upgraded ModelSim after the timing simulation of
the floating point Adder.

Figure 6.1: Output of Single Precision Floating Point
Adder when above input's were given

b) Multiplier

The following snapshot shown in fig 6.2 had been taken
from upgraded ModelSim after the timing simulation of
the floating point multiplier.

Figure 6.2: Output of Single Precision Floating Point
Multiplier when above input's were given

c) Division

The following snapshot shown in fig 6.3 had been taken
from upgraded ModelSim after the timing simulation of
the floating point divisor.

Figure 6.3: Output of Single Precision Floating Point
divisor when above input's were given

d) Sqare Root

The following snapshot shown in fig 6.4 had been taken
from upgraded ModelSim after the timing simulation of
the floating point sqare root.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Figure 6.4: Output of Single Precision Floating Point
square root when above input's were given.

VII. Conclusion

Here, we have added the analog features in the form
of floating point notation, defined in the working library
to the resource library of the CAD tool of ModelSim
PElO.lb successfully. Using this novel tool, different
arithmetic operations have been performed for addition,
multiplication, division and square root on different
floating point numbers. It has been used IEEE 754
standard based floating point representation. The design
algorithms have been coded in VHDL[7]. As a case
study, we have to take any of analog mixed signal DSP
application[8], which could be modeled using the
upgraded ModelSim PE 10.1b tool without using VHDL-
AMS extensions. This type of modeling is called
RMS(Rachuri Mixed Signal) Modeling. There are
significant advantages using RMS modeling, primarily in
the aspects of simulation speed and portability of models
and cost.

References

[1]. D. Goldberg, "What every computer scientist should know about
floating-point arithmetic" pp. 5-48 in ACM Computing Surveys vol. 23-
1 (1991).

[2]. Charles Farnum, "Compiler Support for Floating-Point
Computation" Software Practices and Experience, pp. 701-9 vol. 18,
July 1988.

[3]. M. Leeser, X. Wang, " Variable Precision Floating Point Division
and Square Root", Department of Electrical and Computer Engineering
Northeastern University.

[4]. Taek-Jun Kwon, Jeff Sondeen, Jeff Draper USC Information
Sciences Institute Design Trade-Offs institute "Floating-Point Unit
Implementation for Embedded and Processing-In-Memory Systems"
4676 Admiralty Way Marina del Rey, CA 90292 U.S.A.
[5]. IEEE computer society: IEEE Standard 754 for Binary Floating-
Point Arithmetic, 1985.

[6]. Raguveer M.Rao, Ajit S. Bopatikar "Wavelet transforms
introduction to theory and application, Addison-weswley, 2001.
[7]. J.Bhaskar,"AVHDL primer" Pearson education,2004.
[8] C. Burus, et al. Introduction to Wavelets and Wavelet
Transformation A Primer. Prentice Hall, 1998.

[9] K. K. Parhi, VLSI Digital Signal Processing Systems, Design and
Implementation, John Wiley & Sons, NY, 1999.
[10] System Generator for DSP User Guide UG640 (v4.1) April 24,
2012

[11] Xilinx XAPP467 Using Embedded Multipliers in Spartan-3
FPGAs- May 13,2003.

[12]Pedro Echeverna, Miguel Angel Sanchez, Marisa Lopez-
Vallejo, "Development of a Standard Single Floating-Point Library
and its Encapsulation for Reuse" p99.pdf in

DCIS2009.unizar.es/FILES/CR2.

Authors Profile

Mr.R. Prakash Rao, received
his M.Tech degree from
College of Engineering ,
Andhra University, Vizagjndia
and B.Tech degree from
Siddardha College of

Engineering, Nagarjuna

University ,Guntur,India.
Presently he is working as HOD
in St. Peter's Engineering
College, Hyderabad. He published 08 papers in various
National and International Journals and Conferences. He
got best faculty award during 2009-2010 in ASTRA,
Hyderabad. He has guided 02 M.Tech projects and about
25 B.Tech projects in various levels. His research
interest includes VLSI,Microwave Engineering.

Dr. B.K. Madhavi, received
Ph.D from JNTU, Hyderabad.
She completed ME from
BITS-PILANI in the

specialization of

Microelectronics. She

published 24 research papers
in various National and
International Journals and
Conferences. Presently she is
guiding 10 PhD Students and guided several BTech and
MTech Projects. She is also being reviewed research
papers for IETE. She participated in several workshops,
summer and winter schools, National, International
conferences and also organized several National level
workshops, student paper contests, and seminars etc. Her
research interest includes Microelectronics (VLSI Design,
Low Power VLSI, MixedSignal Processing), Wireless
communications.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

V-Diagnostic: A Data Mining System For Human
Immuno- Deficiency Virus Diagnosis

Omowunmi O. Adeyemo 1

Adenike O. Osofisan

Department of Computer Science

University of Ibadan

Ibadan, Nigeria

1 Correspondence Author: wumiglory@yahoo.com

Abstract — A very serious health problem and life threatening
diseases that has taken over the world medical scene from
early 80s up to the present is Acquired Immune Deficiency
Syndrome(AIDS), which is a result of Human Immuno-
Deficiency Virus (HIV) in the body system. The World Health
Organization through the support of United Nations advocates
avoidance of unsafe sex, use of unsterilized sharp object and
regular tests to ascertain ones HIV status. The campaign on
HIV/AIDS is not effective especially on issues that relate to
diagnosing of HIV at the early stage, it is most threatened
because of discrimination against people living with the virus
and lack of testing and counselling centre, most especially in
rural areas of developing countries like Nigeria. Therefore,
this paper focuses on the development of a Neural Network
Based Data Mining System that could learn from historical
data containing symptoms, mode of transmissions, region and
status of patient which is used to predict or diagnose a patient
HIV status. The system offers a very simple interactive
platform for all any type of users providing self-diagnosis
against this life threatening and deadly virus.

Keywords- Data Mining; Back Propagation Neural
Network, Medical; AIDS; Symptoms and HIV

I. Introduction

The Human Immunodeficiency Virus (HIV) is a pathogen that
results in Acquired Immunodeficiency Syndrome (AIDS). It
has been the most significant emerging infectious agent of the
last century and threatens to continue to create health, social
and developmental problems in the millennium. The virus is
indeed a great challenge to science and mankind. HIV and
AIDS is very harmful to man and therefore reduces the life
expectancy. Current data from sentinel surveillance sites
throughout the countries shows that the virus is still spreading;
both men and women are affected especially young and
middle age and that infection occurs at youthful age and
usually through heterosexual contact. The mode of
transmission has posed enormous challenge to researches. It is
known that the virus can pass from one person to another
through different means. People are told to undergo HIV test
to know if they have the disease, but unfortunately some are
even dead if before the result is out, because most of the
patient does not go for test on time and they may even have

the type of HIV that does not reflect on time and sometimes
the medical procedure may be tasky. HIV/AIDS has high rate
of spreading in sub Saharan African countries, especially
Nigeria. It has affected Nigeria both socially and
economically. The HIV results in the destruction of the body's
immune system rendering it unable to fight off opportunistic
infections and therefore resulting in AIDS.

The aim of this study is to develop a system that can be used
to know the HIV status of a patient. This will serve as another
alternative to assist the doctors for quick intervention in taking
care of the infected patient, and this will in turn reduced the
spread of HIV disease. The model can be deployed to different
Local, State, federal, Teaching Hospitals, non-governmental
organization, and even health centres to allow massive
diagnosis of patient status. This will help to determine the
existence or non existence of the virus in a person and it can
immediately assist the government in their preventive policy
because it will now be easier to know and monitor the trend of
the spread because there will be availability of the model at
anytime. The model will be able to keep the status of each
patient after diagnosis which can help the government at any
time to know the trend of the spread in each location in
Nigeria. This will help to project resources to the appropriate
places by determining the status of each patient instead of
waiting for the laboratory test that might be delayed until the
immune system is totally destroyed. The model is expected to
reduce the death rate and increase the life expectancy of
Nigerians.

1. DATA MINING

Many authors have offered different classifications of the
processes that are collectively known as data mining. The
most appropriate of these definitions seems to be the one that
identifies two classes of data mining processes. These are
descriptive and predictive data mining [5], It has been
suggested that descriptive data mining essentially is a subset
of predictive data mining. That is, in order to perform
predictive data mining successfully, one most probably will
have to perform a descriptive data mining first and then use
the information and the results of this

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

process to complete the predictive data mining. Descriptive
and predictive data mining share several common processes.
Descriptive data mining is very useful for getting an initial
understanding of the presented data. It is an exploratory
process and attempts to discover patterns and relationships
between different features present in the database [5].

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
"competitive evaluation of models," that is, applying different
models to the same data set and then comparing their
performance to choose the best. The Deployment stage
involves using the model selected as being the best in the
previous stage and applying it to new data in order to generate
predictions or estimates of the expected outcome.

Predictive data mining is a super set that should include
descriptive data mining as part of its processes. During the
predictive data mining, the descriptive data mining processes
are used as a prelude to development of a predictive model.
The predictive model can then be used in order to answer
questions and assist the data miner in identifying trends in the
data. What is most interesting about predictive data mining
that distinguishes it from the descriptive data mining is that it
can identify the type of patterns that might not yet exist in the
dataset but has the potential of developing. Unlike the
descriptive data mining that is an unsupervised process,
predictive data mining is a supervised process. Predictive data
mining not only discovers the present patterns and information
in the data it also attempts to solve problems. Through the
existence of modelling processes in the analysis the predictive
data mining can answer questions that cannot be answered by
other techniques. Tools that are used in the predictive data
mining process include decision trees, neural networks,
genetic algorithms and fuzzy systems. In the oil and gas
industry, there are many field related operations that can
benefit from the tools and capabilities that data mining has to
offer [5].

2. RELATED WORKS

Betechuoh et al. [1] compared computational intelligence
methods to analyze HIV in order to investigate which network
is best suited for HIV classification. The methods analyzed are
autoencoder multi-layer perceptron (MLP), autoencoder radial
basis functions (RBF), support vector machines (SVM) and
neuro-fuzzy models (NFM). The autoencoder multi-layer
perceptron yields the highest accuracy of 92% amongst all the
models studied. The autoencoder radial basis function model
has the shortest computational time but yields one of the
lowest accuracies of 82%. The SVM model yields the worst
accuracy of 80%, as well as the worst computational time of
203s. The NFM yields an accuracy of 86%, which is the
second highest accuracy. The NFM, however, offers rules,
which gives interpretation of the data. The area under the
receiver operating characteristics curve for the MLP model is
0.86 compared to an area under the curve of 0.87 for the RBF
model, and 0.82 for the neuro- fuzzy model. The autoencoder
MLP network model for HIV classification is thus found to
outperform the other network models and is a much better
classifier.

The process of data mining consists of three stages:

a. Initial exploration

b. Model building or pattern identification with
validation/verification.

c. Deployment, which is the application of the model to new
data in order to generate predictions.

The exploration stage usually starts with data preparation that
may involve cleaning data, data transformations, selecting
subsets of records and in case of data sets with large numbers
of variables ("fields") the performing of some preliminary
feature selection operations to bring the number of variables to
a manageable range depending on the statistical methods,
which are being considered. Depending on the nature of the
analytic problem, this first stage of the process of data mining
may just be a simple choice of straightforward predictors for a
regression model or to elaborate exploratory data analyses
using a wide variety of graphical and statistical methods in
order to identify the most relevant variables and determine the
complexity and/or the general nature of models that can be
taken into account in the next stage. The model building and
validation stage involves considering various models and
choosing the best one based on their predictive performance
that is explaining the variability in question and producing
stable results across samples. This is actually a very elaborate
process and there is a variety of techniques developed to
achieve this goal. Many of them are based on the so-called

Betechuoh et al. [2] in their paper introduced a new method to
analyse HIV using a combination of autoencoder networks and
genetic algorithms. The proposed method is tested on a set of
demographic properties of individuals obtained from the South
African antenatal survey. When compared to conventional
feed-forward neural networks, the autoencoder network
classifier model proposed yields an accuracy of 92%,
compared to an accuracy of 84% obtained from the
conventional feed-forward neural network models. The area
under the ROC curve for the proposed autoencoder network
model is 0.86 compared to an area under the curve of 0.8 for
the conventional feedforward neural network model. The
autoencoder network model for HIV classification, proposed
in this paper, thus outperforms the conventional feed-forward
neural network models and is a much better classifier.

According to Chaturvedi [4], the Human Immunodeficiency
Virus / Acquired Immunodeficiency syndrome (HIV/AIDS) is
spreading rapidly in all regions of the world. But in India it is
only 20 years old. Within this short period it has emerged as
one of the most serious public health problems in the country,
which greatly affect the socio-economical growth. The HIV
problem is very complex and ill defined from the modelling
point of view. Keeping in the view the complexities of the
HIV infection and its transmission, it is difficult to make exact
estimates of HIV prevalence. It is more so in the Indian
context, with its typical and varied cultural characteristics, and

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

its traditions and values with special reference to sex related
risk behaviours. Therefore, he developed a good model which
will help in making exact estimates of HIV prevalence that
may be used for planning HIV / AIDS prevention and control
programs. In this paper Neuro-Fuzzy approach was used to
develop dynamic model of HIV population of Agra region and
the output generated was reliable..

In Sardari [6], a brief history of ANN and the basic concepts
behind the computing, the mathematical and algorithmic
formulation of each of the techniques, and their developmental
background is presented. Based on the abilities of ANNs in
pattern recognition and estimation of system outputs from the
known inputs, the neural network can be considered as a tool
for molecular data analysis and interpretation. Analysis by
neural networks improves the classification accuracy, data
quantification and reduces the number of analogues necessary
for correct classification of biologically active compounds.
Conformational analysis and quantifying the components in
mixtures using NMR spectra, aqueous solubility prediction
and structure-activity correlation are among the reported
applications of ANN as a new modelling method. Ranging
from drug design and discovery to structure and dosage form
design, the potential pharmaceutical applications of the ANN
methodology are significant. In the areas of clinical
monitoring, utilization of molecular simulation and design of
bioactive structures, ANN would make the study of the status
of the health and disease possible and brings their predicted
chemotherapeutic response closer to reality.

Studies were also carried out on the management of
HIV/AIDS Management in communities [3, 7]. Charles et ah
[3] focused on dimensional modelling of HIV patient
information using open source modelling tools. It aims to take
advantage of the fact that the most affected regions by the HIV
virus are also heavily resource constrained (sub-Saharan
Africa) whereas having large quantities of HIV data. Two HIV
data source systems were studied to identify appropriate
dimensions and facts these were then modelled using two open
source dimensional modelling tools. Use of open source would
reduce the software costs for dimensional modelling and in
turn make data warehousing and data mining more feasible
even for those in resource constrained settings but with data
available.

3. METHODOLOGY

3.1 Data Collection

Data was collected from repositories of HIV inpatients and
outpatient in one of the Nigerian hospital. The data set consist
of input factors or variables and an output variable. The input
factors or variables represent the symptoms that influence the
presence of HIV/AIDS in a person. The input used are: Loss
of appetite, Weight loss, Night sweat, Lymphoma, Recurrent
pneumonia, frequent fever, Skin rash, joint pain & stiffness,

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
Infections, Memory loss. The output is the HIV/AIDS status
of the person.

3.2 Pre-processing

The dataset was cleaned, formatted and normalized before it
was organized into a database. Microsoft SQL Server was
used to construct the database with entity such as patient,
symptoms and patient status. Twelve thousand exemplars were
used for training the system.

3.3 Data Processing

At this stage supervised learning predictive data mining was
employed. A back propagation neural network with one
hidden layer was in developing the system. A thousand epoch
was set for the system. An Artificial Neural Network (ANN) is
a class of very powerful, general-purpose tools that may be
applied to prediction, classification and clustering for decision
making purpose. ANN has been developed as generalizations
of mathematical models of biological nervous systems. A first
wave of interest in neural networks (also known as
connectionist models or parallel distributed processing)
emerged after the introduction of simplified neurons by. The
basic processing elements of neural networks are called
artificial neurons, or simply neurons or nodes. In a simplified
mathematical model of the neuron, the effects of the synapses
are represented by connection weights that modulate the effect
of the associated input signals, and the nonlinear characteristic
exhibited by neurons is represented by a transfer function. The
neuron impulse is then computed as the weighted sum of the
input signals, transformed by the transfer function. The
learning capability of an artificial neuron is achieved by
adjusting the weights in accordance to the chosen learning
algorithm. The basic architecture consists of three types of
neuron layers: input, hidden, and output layers. In feed-
forward networks, the signal flow is from input to output units,
strictly in a feed-forward direction. The data processing can
extend over multiple (layers of) units, but no feedback
connections are present. Recurrent networks contain feedback
connections. Contrary to feed-forward networks, the
dynamical properties of the network are important. In some
cases, the activation values of the units undergo a relaxation
process such that the network will evolve to a stable state in
which these activations do not change anymore. A neural
network has to be configured such that the application of a set
of inputs produces the desired set of outputs. Various methods
to set the strengths of the connections exist. One way is to set
the weights explicitly, using a priori knowledge. Another way
is to train the neural network by feeding it teaching patterns
and letting it change its weights according to some learning
rule. In figure 1, a feed forward neural network is presented
having four input layers, five hidden layers and one output.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Input
Layer

Hidden Layer

Input #1

Output
Layer

f ) — Output

Figure 1: A Feed-Forward Neural Network

4. V-Diagnostic Tool

The system is a Data Mining System for Human Immuno-
Deficiency Virus Diagnosis. The system user interface has
three menus: 1. Patient 2. Operation; and 3. Statistics. At the
Patient side as presented in figure 4, symptoms for each
patient can be selected and prediction performed. In the
operation menu as presented in figure 3, data can be generated
to train the ANN system. This data can thereafter be used for
training the data and even cross-validated. The third menu
contains components that can be used to monitor statistics and
prevalence of HIV. It has a module that records each
prediction made as well as location of patients.

In this predictive system, the neural network was used to
create and train a MLP neural network architecture. The
network implemented consisted of an input layer, representing
different inputs symptoms of individuals, mapped to an output
layer representing the HIV status of individuals via the hidden
layer. The network thus mapped the input of individuals to the
HIV status. An error, however, exists between the individual's
predicted HIV status (output vector) and the individual's
actual HIV status (target vector) during training, which can be
expressed as the difference between the target and output
vector. The output of prediction reports either "The result of
this system has shown that you are HIV negative" or "result of
this system has shown that you are not HIV negative." The
latter means the person has the HIV while the former means
the person does not have HIV.

-' " ■ ' ■ ■'- •■— -

Figure 2: V-Diagnostic System

> ;Wdt

RUlb£a Bl ii iU

Oka

* 1 i.

I j«W j Wm

Figure 3: Training of Neural Network

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
84441, Proceedings, 2003 SPE Annual Conference and
Exhibition, October 4-October 8, Denver, Colorado.

SB!

m
m

w
m
m
m

:»
SON
3007

m
m

TtfirusitiiltliBsplamhasdtijimtMipiiafErafHIVrajaSve

[6] Sardari S, Sardari D. (2002). Applications of artificial
neural network in AIDS research and therapy. Curr Pharm
Des., 8(8):659-70.

[7] Vararuk A., Petrounias L. and Kodogiannis V. (2007).
Data mining techniques for HIV/AIDS data management
in Thailand. Journal of Enterprise Information
Management , Volume 21 (1).

Figure 4: Diagnosis of HIV using Neural Network

5. Conclusion

In this work, an artificial neural network was used to diagnose
patient of their HIV status. The data used for training was
obtained from some the hospitals across Nigeria. The neural
network generated reliable predicted output as a result of
series of test carried out and the accuracy of prediction. The
system cannot only be used to determine patient HIV status,
but can also be used to monitor HIV prevalence. Based on the
system output, back propagation feed forward neural networks
forms a good system that can be used to diagnose HIV. In
future research, we are working on feature selection and
optimization of the solution.

REFERENCES

[1] Betechuoh, B.L. Marwala T. And Manana, JV.
(2008). Computational Intelligence for HIV Modelling.
International Conference on Intelligent Engineering
Systems INES 2008 on page(s): 127 - 132

[2] Betechuoh B.L., Marwala T. and Tettey T. (2006).
Autoencoder networks for HIV classification, current
science, Vol. 91, No. 11, 10 December 2006.

[3] Charles D. Otine, Samuel B. Kucel, Lena Trojer. (2010).
Dimensional Modeling of HIV Data Using Open Source.
World Academy of Science, Engineering and Technology
(63)2010.

[4] Chaturvedi D.K. (2005). Dynamic Model of HIV/AIDS
Population of Agra Region. September 2005

[5] Mohaghegh, S., (2003), Essential Components of an
Integrated Data Mining Tool for the Oil and Gas Industry,
With an Example Application in the DJ Basin. Paper SPE

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

A WEB-BASED SYSTEM TO ENHANCE THE MANAGEMENT OF ACQUIRED
IMMUNODEFICIENCY SYNDROME (AIDS)/ HUMAN IMMUNODEFICIENCY
VIRUS (HIV) IN NIGERIA

Agbelusi Olutola
Department of Computer Science,
Rufus Giwa Polytechnic,
Owo, Nigeria.
tola52001@yahoo.com
Aladesote O. Isaiah,
Department of Computer Science,
Rufus Giwa Polytechnic,
Owo, Nigeria.
lomjovic@yahoo.com

Makinde O. E.

Department of Computer Science,

Ajayi Crowther University,

Oyo, Nigeria

Oludayo_makinde@yahoo.com

Aliu A. Hassan

Department of Mathematics & Statistics,

Rufus Giwa Polytechnic,

Owo, Nigeria.

ahaliu@ymail.com

ABSTRACT: Acquired Immunodeficiency
Syndrome (AIDS), a global disease, caused by
the Human Immunodeficiency Virus (HIV) is
arguably the greatest health problem of this
age and there is need to make first class
information on the management of HIV/AIDS
available through the use of Web-Based
Technology. This paper examined the various
ways of contacting HIV and the effort made by
Information and Technology to make life
easier for people living with the virus in
Nigeria. Questionnaires were distributed to
Doctors and people living with HIV/AIDS to
access their knowledge and belief about the
said disease. MySQL was used to generate the
database, to store all the vital information
about the patients, their Doctors and their
complaints.

PHP programming for the implementation of
the interfaces, Dreamweaver HTML for the
design of the web-based application, T-test
and Microsoft Excel were used for the analysis
of data collected. The study looked into the
occupation, age range and the marital status
of different categories of people living with the
virus. It was discovered that there were quite
large numbers of people who are living with
the virus.

Keywords: HIV; t-test; database; Web-based
technology; Serodicordant couple;

Antiretroviral Therapy (ART); MySQL; PHP;
HTML.

INTRODUCTION

Acquired Immunodeficiency Syndrome
(AIDS), a global disease, caused by the
Human Immunodeficiency Virus (HIV) is
arguably the greatest health problem of this
age, since the first case was diagnosed in the
USA in 1981, the disease has spread
dramatically with cases reported worldwide
[4] . Situations with HIV/AIDs have made it
imperative for countries like Nigeria to evolve
and strengthen strategies for care and support
of people living with the disease. The
development of Antiretroviral Therapy (ART)
cast a big ray of hope in clinical management

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

of HIV/AIDS. ART does not really cure the
disease but when used in proper combination,
can reduce the replication of the virus and
enhanced restoration of immune system in the
infected individual. The optimal combination
of ART which is known as HAART (Highly
Active Antiretroviral therapy) has significandy
reduced the mortality in people living with the
virus. The beneficial attributes of HAART
have encouraged several developed and
developing countries to adopt its use [4].

In Nigeria, the first two cases of
HIV/AIDS were identified in 1985 and were
reported at an international HIV/AIDS
conference in 1986. In 1987, the Nigerian
health sector established the National Aids
Advisory Committee which was shortly
followed by the establishment of the National
Expert Advisory Committee on AIDS
(NEACA) [8].

The three main transmission routes for HIV in
Nigeria include:

* Heterosexual sex: Approximately 80-

95 percent of HIV infections in Nigeria
are as a result of heterosexual sex.
Factors contributing to this include: a
lack of information about sexual health
and HIV, low level of condom use, and
high level of sexually transmitted
diseases. Women are mostly affected
by HIV. In 2009, women accounted for
56 percent of all aged 15 and above
living with the virus.

* Blood transfusions: HIV transmission
through unsafe blood, account for the
second largest source of HIV infection
in Nigeria. Not all hospitals in Nigeria
have the technology to effectively
screen blood, and therefore there is a
risk of using contaminated blood. The
Nigerian Federal Ministry of Health
has responded by backing legislation
that requires hospitals to only use
blood from National Blood
Transfusion Service, which has far
more advanced blood-screening
technology.

* Mother-to-child transmission. Each
year about 57,000 babies are born with
HIV. It is estimated that 220,000
children are living with HIV in
Nigeria, most of who became infected
from their mothers. (Weekly news
digest by Global advocacy for HIV in
Nigeria (2010)).

PROBLEM DEFINITION

One of the recently debated issues has been
the extent of HIV/AIDS epidemic in Nigeria
[3]. Despite the fact that some scientists have
made effort to manage AIDS with HAART
(HAART - Highly Active Antiretroviral
therapy), some infected individuals still
succumb to death due to reasons such as:
ignorance, lack of information on how to get
access to HIV/AIDS treatment and care, lack
of power and control for women, lack of

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

proper information on nutrition and exercise,
discrimination and rejection from the general
society and lack of courage to go for
counseling.

The need to work on the above listed problems
is necessary and important for any nation to
control the number of people infected with
HIV/AIDS and help those already living with
the virus to live a longer possible life.
The improvement in life expectancy brings
scrutiny on issues of long term drug toxicity.
High among those concerns is the possibility
of progressive Neuro cognitive damage
associated with HIV, ART effectively reduces
HIV RNA in cerebrospinal fluid, as well as in
plasma; however, the effect in intrathecal
immuno activation is less well studied.
Another concern regarding the increasing
numbers of individuals receiving ART is the
possibility of transmission of drug resistant
variance. Drug resistant viruses may decline to
a level undetectable by conventional
sequencing (minority resistance variant). The
field has now focused on the best approach to
identify minority variant and whether their
detection is important for both variant and
patient management.

A proposed web-based user interface for the
management of HIV/AIDS is designed to
solve the above stated problem. This system is
expected to provide the following solutions:
adequate and first hand information about
HIV/AIDS (i.e causes, treatments,
preventions, mode of transmission, latest news

on the virus etc), an interface where a patient
can communicate with the doctor, latest health
news and medication request details.

REVIEW OF RELATED WORKS

Antiretroviral drugs can have toxic side
effects, however, there is no evidence that
anti-HIV drugs cause the severe immune
deficiency typical of AIDS. There are
abundant evidences that currently
recommended that causes of antiretroviral
therapy can improve the length and quality of
life of HIV positive people.
Trinidad and Tobago has recently been taking
steps to combat HIV/AIDS. A national
consultation has resulted in a five year
National HIV/AIDS strategic plan. This
recognizes that HIV/AIDS is a development
issues and seeks a holistic, expanded and
coordinated response.

An HIV prevention program for Africa-
America women was to test the efficacy of a
sexual risk reduction intervention to enhanced
safer sex practices and reduces sexually
transmitted infections (Chlamydia, gonorrhea
and trichomonas) among African-American
HIV serodicordant couples. The 8-session HIV
prevention program focuses on enhancing
cultural/ethnic pride, HIV transmission risk-
reduction knowledge, couples sexual
communication skill, male and female condom
use self- efficacy and relationship satisfaction.
Couples are observed for 1-year to assess
changes in risk behaviors, psychosocial

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

mediators and sexually transmitted infections
1-year after the HIV prevention program.
[1] investigate the management of HIV
through the use of data mining techniques,
patterns in HIV/AIDS patient data. These
patterns can be used for better management of
the disease and more appropriate targeting of
resources.

[10] used the advanced statistical techniques
and the development of additional
technologies for assessing the biological
aspect. The nervous system-immune system
relationship should enable PNI (psycho neuro
immunology) to evolve. In turn, this will
enable clinicians to better assess their patients'
needs and treat their diseases.
Laurie and his colleague used quantitative
research techniques to build a capacity of
Kenyan institution to carry out HIV/AIDS
prevention and control activities by
strengthening and institutionalizing IEC
(Information, Education and Communication)
media and materials development skill among
the programme staff of Kango.
[6] uses Multi Layer Backward Neural
Network Model (MLFB) by back propagation
algorithm to describe the regimens
specification for the HIV/AIDS patients, based
on the patients' unique factors like age,
weight, HB, CD4 AND CD8.
[5] examined both HIV-positive and HIV-
negative subjects who were either heavy
drinkers or light drinkers. Her study assessed
the levels of two molecules called PCR and

ATP, which reflect the cell's energy
metabolism as well as of a group of
compounds called PDE, which represent
breakdown products of molecules found in the
cell membranes. Her study found that chronic
consumption was associated with lower
concentrations of PDE, PCR and ATP in white
matter of the region surrounding the
ventricles.

Eileen and her colleagues worked in the
respond to diagnostic and therapeutic
advances to improve standardization and
comparability of surveillance data regarding
persons at all stages of HIV disease.

THE WEB-BASED SYSTEM

Web-based system technically refers to those
applications or services that are resident on a
server that is therefore accessible from
anywhere in the world via the web. As a
matter of fact, it provides a very good
interface to enable easy interaction between
the system and the user. The system provides
features that are specifically designed to
enhance the management of people living with
the virus and also provide adequate
information that will bring about reduction in
the spread of the virus. When the website is
uploaded; the home page is the first page that
will come up where the user signs in by
supplying username and password.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

RESEARCH METHODOLOGY

The study was carried out at the Federal
Medical Centre, Owo in Ondo State. An
extensive literature review on the management
of HIV was carried out. The collection of data
used is through the administration of
structured questionnaire by the Doctors and
the HIV/AIDS patients, basically to access the
knowledge and belief about the virus. The
following tools were used during the course of
this research work: MySQL, to generate the
database used to store all the vital information
about the patients, their Doctors and their
complaints, PHP programming was used for
the implementation of the interfaces,
Dreamweaver HTML; for the design of the
web-based application and t- test was used to
carry out the comparison analysis test between
the respondents of HIV positive and negative
respond through the use of SPSS 17 for the
analysis of data collected and the Microsoft
Excel was adopted to depict the data
presentation of the data collected.

ANALYSIS OF THE QUESTIONAIRE

Two structured questionnaires were used to
gather information from each of the factors
concerned during the course of this research
work, one for the Doctors and the second one
is for the people living with the virus. Ms-
Excel was used for the analysis of the data
collected.

Fig 1.1

Marital Status

60%
50%
40',%
30%
20%
10%
0>i

■

Mania Status

// "

Fig 1.2

Age Range

I Age Range

-5 s ^ <$ £>''

Fig 1.3

Occupation

<£ <A> t?

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

Table (i)

Paired Samples Statistics

Std.

Std. Error

Mean

Deviation

Mean

Pair HIV

21.47

11.532

2.977

1 Positive

HIV

17.00

12.689

3.276

Negative

Table (ii)

Paired Samples Test

Paired Differences

95% Confidence

Interval of the

Std.

Std. Error

Difference

Mean

Deviation

Mean

Lowe,

Upper

Sig. (2 -tailed)

Pair
1

HIV Positive -
HIV Negative

4.467

23.625

S.100

-8.616

17.550

0.732

0.476

Having observed from the paired-wise
samples test result obtained from the table (i)
& (ii) above, the average values with the
variability's (standard deviation value) are
(21.47, 11.532) and (17.00, 12.689) for HIV
positive and negative respectively and the t-
value (0.732) obtained is not significant at
0.476 test significant value. The output result
shows that there is no different in standard of
living in the response of the respondent
concerning HIV positive and negative that
undergoes treatment in the hospital.

RESULTS AND DISCUSSIONS

Figure 1.1 shows the marital status of people
living with HIV/AIDS. The chart analysis
shows that 35% are divorced men and women,
50% are single and 15% are married among
those that are living with the virus.
Figure 1.2 shows the age range of people
living with the virus. The chart shows that
between the ages of 0-10 is 5%, ages 21-31 is
20%, ages 22-35 is 50% and 36 and above is
25%.

Figure 1.3 presents occupation of HIV/AIDS
patients. 15% belong to people in private
services, 40% are students, 30% are public
servants and 15% are dependant.

Table (i) and (ii) output results show that there

is no significant in the response of the

respondent concerning HIV positive and

negative in term of living condition with the

treatment undergoes in the hospital.

The different categories of analysis discussed

above shows that there is a need for quick and
urgent intervention of Information and
Communication Technology to make first
class information and awareness about
HIV/AIDS available to all the people living
with the said virus through the use of Web-
based technology. This will go a long way in
managing people living with HIV/AIDS and
as well as bringing about reduction in the
spread of the disease.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

REFERENCES

[1] A. Vararuk, I. Petrounias, V. Kodogiannis, v.

"Data Mining Techniques for HIV/AIDs Data

Management in Thailand", Journal of Enterprise

Information Management. Vol. 21, Iss:l, pp. 52 -

70, October 2009.

[2] Aids control and prevention (AIDSCAP)

project, final report for the AIDSCAP program

in Kenya. Arlington, VA: family health

international. 1997.

[3] B. Laurie, "Surveillance case definitions for

HIV infection and AIDS REVISED", Med Scape

Medical New. December 10, 2008.

[4] J. Durgavish et al, "Nigeria: Rapid

Assessment of HIV/AIDS care in public and private

sectors", US Agency for International Development.

August 2004.

[5] J. Dieter, "Effect of alcohol and HIV infection

on central Nervous system", National institute on

Alcohol Abuse and alcoholism of the National

Alcohol Research & Health. Vol. 25, No 4, 2001.

[6] J. Gehrke, R. Ramakrishnan, "Database

Management System" second edition, McGraw-Hill

Higher Education. 2000.

[7] M. Lilly, P. Balasubramanie, "Multilayer Feed

Backward Neural Network Model for Medical

Decision Support: Implementation of back

propagation Algorithm in HIV/AIDS regimen.

International Journal of Reviews in Computing.

Vol. 1, December 2009.

[8] O. Adeyi et al, "AIDS in Nigeria: A nation on

the threshold: The epidemiology of HIV/AIDS in

Nigeria", Harvard Center for Population and

Development Studies.2006.

[9] O. G. Lala et al, "Web-Based Systems
for the Management of HIV/AIDS", 4 th
International Conference on ICT Applications
(AICTTRA). Pp 140 - 150, September 2009.
[10] R. Seth et al, "Psychoneuro
Immunology: An analysis of HIV and
Cancer", URJHS vol. 9, 2008.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 6, June 2012

APPENDIX

Home page

Doctor page

Patient page

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Data Mining System For Quality Prediction Of
Petrol Using Artificial Neural Network

Omowumi O. Adeyemo 1 Adenike O. Osofisan Ebunoluwa P. Fashina

Kayode Otubu

Department of Computer Science

University of Ibadan

Ibadan, Nigeria

Correspondence Author: wumiglory@yahoo.com

Abstract — The increasing cry of the masses over poor quality
of petroleum products most especially petrol has poised
researchers and refinery engineers to devise a way of telling
the class of quality of products expected from a sample crude
oil without having to refine it. To this end, a system that can
predict the quality and class of petrol expected from a sample
crude oil is desired. Getting such accurate predictions for the
class and hence the quality of petrol however can be tasking
for humans. This work presents a data mining system, which
implemented a multi-layer neural network trained with the
back propagation training algorithm. The focus, however, was
on petrol because of its significance and wide usage. The
outcome generated by the system shows that multilayer
perception back propagation neural network could
successfully classify and predict the quality of petrol.

Keywords- Petrol; Multilayer Perceptron; Data Mining;
Quality; Back Propagation

I. INTRODUCTION

Today, organizations are accumulating vast and growing
amounts of data in different formats. The patterns,
associations, or relationships among all these data can provide
information. However, the vast and fast-growing amount of
data normally exceeds human ability for comprehension and
analysis without powerful tools. As a result, data collected in
large data sources become "data tombs"- data archives that are
seldom visited. Even when the databases serve as information
sources, poor decisions are made because the decision makers
do not have appropriate tools to extract the valuable
knowledge embedded in the vast amount of data.

In fact, refinery engineers have based decisions on crude oil
refining on the rule of thumb for many years. With the
invention of data mining, the challenges are surmountable.
Data Mining refers to the nontrivial extraction of implicit,
previously unknown and potentially useful information from
data in databases [7]. It is a key step of knowledge discovery
in databases (KDD). In other words, data mining involves the
systematic analysis of large datasets using automated methods.
By probing data in this manner, it is possible to prove or

disprove existing hypotheses or ideas regarding data or
information while discovering new or previously unknown
information. It is noted for its Pattern Recognition ability that
ensures that information is obtained from vague data [3], In
particular, unique or valuable relationships between and within
the data can be identified and used proactively to categorize or
anticipate additional data.

1.1 ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks (ANNs) are biologically
inspired structures composed of elements that perform in a
manner analogous to the most elementary functions of the
biological neuron. ANN can modify its behavior in response to
the environment. Thus, given a set of inputs (and perhaps with
desired outputs), ANN self-adjust to produce consistent
responses. ANNs are capable to perform tasks like learning,
memorize, experience and generalize. Neural Networks, also
known as Neural Computing, is a field of research in artificial
simple intelligence. It is the study of networks of adaptable
nodes which, through a process of learning from task
examples, store experimental knowledge and make it available
for use. A Neural Network is a group of processing elements
where one subgroup makes independent computations and
passes the result to a second subgroup. Each subgroup may, in
turn, make its independent computations and the result to yet
another subgroup. Finally, a subgroup of one or more
processing elements determines the output of the network.

Neural Computing derives its name from the fact that
it is a field that tries to mimic the functions that the biological
neural system of the human brain performs. Neural Networks
have been able to exhibit some very interesting and important
features that are peculiar to the brain. One such feature is
learning. It is necessary at this point to address the need to
imitate the biological neural system, as adopted in neural
computing ([8].

Learning, for example, is the way by which, as
children, we pick up speech, learn to write, eat and drink and
develop our own set of standards and morals. On the other
hand, learning in computer systems often requires the building
of a rule-base which must provide for all possible
combinations that are often endless [4]. Artificial Neural
Networks (ANN) , which emerged as a major paradigm for

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

data mining applications were inspired by biological findings
relating to the behavior of the brain as a network of units
called neurons.

While there are numerous different (artificial) neural
network architectures that have been studied by researchers,
the most successful applications in data mining of neural
networks have been multilayer feed-forward networks. These
are networks in which there is an input layer consisting of
nodes that simply accept the input values and successive
layers of nodes that are neurons. The outputs of neurons in a
layer are inputs to neurons in the next layer. The last layer is
called the output layer. Layers between the input and output
layers are known as hidden layers. Figure 1 presents a diagram
for this architecture.

Figl. Multilayer Neural Networks

Neural networks are of particular interest because they offer a
means to efficiently model large and complex problems in
which there may be hundreds of predictor variables that have
many interactions. Neural nets may be used in classification
problems (where the output is a categorical variable) or for
regressions (where the output variable is continuous).

1.2 RELATED WORKS

Artificial Neural Network (ANN) has been applied in
several areas of crude oil content prediction. One of it is the
work done by Linde et al. [2] where ANN was used for Air-to-
Fuel Ratio (A/F) Estimation in Two-Stroke Combustion
Engines. Though most of the larger engines in automobile
have sensors but there are a number of problems with these
sensors. Part of the problem is that it is expensive, slow,
sensitive to pollution and gives only a binary input i.e.
indicating whether the A/F is above or below a factory set
value. This necessitates the need to seek for other ways of
measuring Air-to-fuel Ratio. They used ion-current
measurements and artificial neural networks to estimate A/F is
developed and evaluated. The tests have also shown that it is
possible to extract other information from this signal, like
misfiring, the fuel quality, and others. The result should be
seen as a first step towards a complete, self-tuning engine
control system.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Commuri et al. [5] developed a neural network-based
Intelligent Asphalt Compaction Analyzer (IACA). IACA was
a novel neural network-based approach. It is contrary to
existing techniques where a model is developed to fit the
experimental data and to predict the density of the mat. Their's
was a model-free approach which used pattern-recognition
techniques to estimate the density. The neural network was
first trained using several vibration patterns corresponding to
different density levels to extract the features from the
vibrations of the compactor and used these features to estimate
the level of compaction. The IACA output was continuously
available to the operator in real time and could serve as a
useful guide during the compaction process.

She et al. [6], proposed an expert control strategy
based on a combination of back propagation networks,
mathematical models and rule models to compute and track
the target percentages accurately. The previously used
conventional computation methods involve constructing
mathematical models to predict quality based on measured
data for coal blending and distillation, and then computing the
target percentages using the models. The models mainly
employed linear system identification techniques, such as the
least-squares method. However, it is difficult to get accurate
percentages by conventional methods because the computation
is based solely on the mathematical models, which do not
describe the exact relationships among the parameters that
characterize the quality of the coal blend and coke, and the
quality and percentage of each type of coal. The system used
empirical knowledge to solve the control problem. The
strategy was implemented in a hierarchical configuration with
two controllers that does not have the drawbacks of the
conventional methods.

In another related work, Akinyokun et al. [1] used an
Unsupervised Self Organizing Map (SOM) of neural networks
for the determination of oil well lithology and fluid contents.
Their work was based on fuzzy inference rules derived from
known characteristics of well logs. The application was
justified because the interpretation of the clusters generated by
the SOM neural networks represents the characterization of
the contents.

Despite the contributions of these works, none has
been able to result in a generic and robust intelligent system
that can analyze the huge amount of crude oil data and predict
quality of petrol expected from a given crude oil. These are
achievable using multilayer perceptron neural network whose
topology can be altered at any time and generate very accurate
prediction. This kind of system is required by refiners who
require a powerful and robust tool that can help analyze the
huge amount of data in an attempt to predict the class and
quality of petrol expected from a given sample of crude oil.
With such predictor, the refiners can tell if the desired class of
petrol can be obtained from the sample crude oil without
having to refine it. This of course eliminates incurable of more
cost of computing and products.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

1. METHODOLOGY

The data used for the prediction is crude oil
exploratory data obtained from a refinery in Nigeria. Data
Preparation is performed on the acquired data. The data
acquired is highly susceptible to noise, and inconsistent. This
is due to the huge size or human error. Thus, data to be fed
into the Neural Network has to be preprocessed in order to
help improve the accuracy of the algorithm. There are a
number of data preprocessing techniques. They include data
cleaning, data integration, data reduction, and data
transformations. This work performs data transformations,
specifically normalization (Min-max normalization). The
model algorithm is back propagation and can only work on
data input within the range of and 1. Therefore Min-max
normalization is performed to transform the attribute data. In
the normalization, attribute data are scaled so as to fall within
a small specified range of -1.0 to 1.0 and 0.0 to 1.0. This is
linear transformation. It improves the accuracy and efficiency
of the mining algorithm. Min-max normalization, used for this
project, performs a linear transformation on the original data.
This is done to transform the attributes into a form usable for
model algorithms.

Since there are no clear rules as to the number of
hidden layer units, this work uses Neural Network with 1 layer
each for the input, hidden and output. Network design is a
trial-and-error process and may affect the accuracy of the
resulting trained network. The initial values of the weights
may also affect the resulting accuracy. Once a network has
been trained and its accuracy is not considered acceptable, the
training process is repeated with a different network topology
or a different set of initial weights. In this work, Multi-Layer
Perceptrons (MLPs), a special architecture of ANNs are
implemented using backpropagation algorithm. This work
implements two versions (modes) of the back-propagation
algorithm they are Pattern-by-Pattern Mode and Batch Mode.
Since the result or output is foreknown, a learning that is
guided by knowing what we want to achieve, is known as
supervised learning.

Given the topology of the network (number of layers,
number of neurons per layer) and the type of activation
function used, the synaptic weights (which in general are
randomly set at the beginning) are then adjusted so that at the
next iteration the output produced by the network are closer to
the desired output. The ultimate goal of the training procedure
is to minimize the observed error between the desired output
and the actual output produced by the network. At the
termination of the training process, the neural network has
learnt to produce an output that closely matches the desired
output. Then the network's structure is frozen and the network
becomes operational, ready to be used for prediction of oil
quality from the properties of the crude oil. It is to be
emphasized that Prediction is made by specifying the
properties of the crude oil obtained from laboratory test on the
crude oil. The system outputs the density of the petrol
expected from the sample crude oil. Based on this, it further
classifies the petrol as light, medium or heavy petrol.

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
2. IMPLEMENTATION

The main interface of this application is shown in
figure 2. It has four menu options that provide various
functionalities. The first one is the File Menu, it enables the
user to save network data, exit application and reset the
memory of the Neural Networks. The Network Menu enables
the user to build his desired neural network, by specifying the
number of neurons in the input layer, the number of neurons in
the hidden layer, the number of neurons in the output layer. It
allows user to specify his Training Method. It also allows the
user to load or randomize weights and thresholds that the
Neural Network uses initially. Inputs for other Network data
like the learning rate, the momentum, number of epoques, and
the number of data are also taken using this menu. It allows
users to analyze the network. The Parameter-Setup Menu
allows user to change the network parameters. The Help Menu
allows the user to view simple instructions about the system.
Figure 4 is the platform that allows users to specify the
network parameters. The user launches the system, specifies
the network topology and creates the neural network as
presented in the figure 3. This allows user to specify his
choice of network. User then proceeds to the process of
making predictions by clicking the Menu item.

This prompts the user to specify the thresholds
(biases), weights to be used initially by the neural network as
presented in figures 5- 9. In figures 10-14, the threshold for
input, hidden and output layer are generated. The training data
is then requested to be loaded. Data can be randomly
generated or loaded from text files. On presentation of inputs
for building the network topology and the initialization of
parameters, the training data are then loaded into the built
network to be trained. After training ends, the training
information is displayed as presented in figure 15- Training
data can be loaded from text files or be randomized, but this
does not give accurate results. The training is then performed
and this yields a Learned Neural Network. The altering page is
presented in figure 16.

The network is tested by comparing the output
expected with the network output. The output is then presented
to user in a readable format for acceptability of the network
accuracy. This gives a learned network. With the accuracy of
the network ascertained as presented in figure 17, the system
is suitable for making prediction of oil quality.

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Fig2. Main Interface

Create Neural Network X

Number of input neurons: 3

Number of hidden neurons: 3

Number of output neurons:|s

1 OK i |

Cancel

Fig3. Network Topology Design window

Parameters

|x|

Learninc

Rate n:

0.9

Moment

jm a:

0.5

Number

of epoques;

Number

of patterns:

Cancel

H Weights for input to hidden lave

Creating a neural network 8x3x8

Enter the weights from the input to the hidden layer, below

input /hi,.. | 1 2 3

Load Data , , ,

Save Data ...

Random ,,,

[ OK ][ Cancel ]

Fig5. Initial Weights Input for the network

Random Filling of table j

Minimum Value:
Maximum Value:

-0.1

0.1

OK Cancel

Fig6. Range of Initial Weights (Hidden to Output)

gWeig

Creating a neural network 8x3x8

Enter the weights from the hidden to the output layer, below

hidden / .

12 3 4

-0.0288681...

0.06835600...

-0.0220306...

0.046431

0,009178%,,,

-0,0486794,, ,

-0, 0992755,, ,

0,01300:

-0.0640489...

-0.0530237...

0.03527292...

0.00274!

Load Data . . .

Save Data ...

) Random ... ||

.<l

mi >

[ OK

|| Cancel |

Fig4. Network Parameters Input window

Fig7. Weights are generated

Random Filling of table

Minimum Value:
Maximum Value:

-0.1

0.1

Cancel

Fig8. Range of Initial Weights (Hidden to Output)

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

M Weights for hidden to output Layer |

Creating a neural network 8x3x8

Enter the weights from the hidden to the output layer, below

hidden/,,, | I 2 3 | 4

-0,0288681,, ,

0.06835600...

-0.0220306,, ,

0,04643

0,00917896,, ,

-0.0486794,, ,

-0.0992755,, ,

0,01300

-0,0640489,,,

-0.0530237,,,

0.03527292...

0,00274

Load Data . . .

Save Data,,,

| Random , , , ||

OK || Cancel

Fig9. Weights are generated

M Thresholds for hidden layer

Creating a neural network 8x3x8

Enter the thresholds for the hidden layer below

hidden

Load Data .

Save Data ,

Random ,

OK Cancel

FiglO. Thresholds Input

Random Filling of table I

Minimum Value:
Maximum Value:

-1

Cancel

Figll. Range of Thresholds are specified

Thresholds for output layer j

Creating a neural network 8x3x8

Enter the thresholds for the output layer below

output 12 3 4

1 1 1 1

Load Data , . ,

Save Data .,,

Random ,,.

< . >

OK ] Cancel ]

Figl2. Thresholds are generated

Random Filling of table

Minimum Value: -O.l

Maximum Value: O.l

OK Cancel

Figl3. Range of Thresholds are generated

Thresholds for output layer

Creating a neural network 8x3x8

Enter the thresholds for the output layer below

output

I 1

-0.0885574,,, 0,00526278,,, 0,06578104,,, -0.09671

Load Data ,

Save Data ,

I Random ,

Cancel

FiglA Thresholds are generated

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

O Patterns

Creating a neural network 8x3x8
Enter the patterns below

patterns 1 | 2 3

0.07240721..

0.78832348...

0.62001627...

0.97191

0,01733016,,

0.19703779...

0.13003354...

0,84916

0.18050105..

0.16754723...

0.93841272...

0.60198

0.19741643..

0.18474043...

0.37882149...

0.52893

Load Data . . .

0,06031774,,

0.95894342...

0.07560578...

0,08612

5ave Data ...

0.14410095..

0.47453412...

0.50143748...

0.01838

0,20488208,,

0.16746316...

0.07011841...

0,68178

| Random ...

0.85121920..

0.73168076...

0.26454055...

0.05759

0.98799055..

0.05447011...

0.29980237...

0.95762

0,73292902,,

0.78068677...

0.82148174...

0,92096

< l

[ OK J| Cancel j

Figl5. Training Data generated

r. M- - i f i . ". ii,- i,

HSUS)

| Cg CLICK HERE Qf

Alter Parameter

OMY^

fVetv^a^e

Momentum

"""—'-

L„,„i„ 0M . E

L.„„in R, to

Learning Epoch |

L .„„„«„™ h ^

| Reset | | Alter |

Figl6. Altering the network topology

O Testing Neural Networks

NETWORK OUTPUT
0.6950523168945877
0.56590004 6 6972 93 1
0. 19252 674790173752
0.442 53907775852797
0.36135062823062103

EXPECTED OUTPUT
0.6960523168945877
0.5669000466972931
0.19352 674790173752
0.44353 907775852 797
0.36235062823062103

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012
sample, it will also prevent the need to refine crude oil that
will not yield the desired petrol. It thus enhances a cost-
effective refining process.

In future, this work will be extended by comparing multilayer
perceptron neural networks with other artificial neural
networks to get the best prediction. Also, we will combine
neural networks and fuzzy logic to obtain useful information
from fuzzy data.

References

[1] Akinyokun O.C., Enikanselu PA., Adeyemo A.B. and
Adesida B. (2009) "Well Log Interpretation Model for the
Determination of Lithology and Fluid Contents". Pacific
Journal of Science and Technology. 507-517.

[2] Linde A., Taveniku M., and Svensson B. (1992). Using
Neural Networks for Air-to-Fuel Ratio Estimation in
Two-Stroke Combustion Engines.

[3] Baker B. "Forensic Audit and Automated Oversight",
Office of Auditor General based on logistic model tree.
JBiSE. Vol.2, No.6, 2009, pp. 405-411.

[4] Bansal K., V.adhavkar S, and Gupta A. (1998), Neural
networks based forecasting techniques for inventory
control applications. Data Mining and Knowledge
Discovery, 2(1):97-102.

[5] Commuri S., Mai A.T., and Zaman M. (2007), A Novel
Neural Network-Based Asphalt Compaction Analyzer,
Int. J. Pavement Engineering.

[6] She J., Min W., Nakano M. (1999),A Model-Based
Expert Control Strategy Using Neural Networks for the
Coal Blending Process in an Iron and Steel Plant. Expert
System with Applications, Vol. 16, No. 3, pp. 271-281.

[7] Zaiane O. R. (1999) Principle of Knowledge Discovery in
Databases, University of Alberta. Department of
Computer Science. CMPUT690.

[8] Pujar A.K. (2001), Data Mining Techniques, University
Press, 1st Edition, 2001.

'ig 17. Test Result Displayed

3. CONCLUSION

This work has shown that the strength of Neural Network to
mimic the human brain and make accurate predictions cannot
be over-emphasized. Its application, as applied in this work
has shown that refinery engineers can predict the quality of
crude oil expected from a crude oil sample. Not only will such
predictions tell the quality of petrol expected from a crude oil

http://sites.google.com/site/ijcsis/
ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

IJCSIS REVIEWERS' LIST

Assist Prof (Dr.) M. Emre Celebi, Louisiana State University in Shreveport, USA

Dr. Lam Hong Lee, Universiti Tunku Abdul Rahman, Malaysia

Dr. Shimon K. Modi, Director of Research BSPA Labs, Purdue University, USA

Dr. Jianguo Ding, Norwegian University of Science and Technology (NTNU), Norway

Assoc. Prof. N. Jaisankar, VIT University, Vellore.Tamilnadu, India

Dr. Amogh Kavimandan, The Mathworks Inc., USA

Dr. Ramasamy Mariappan, Vinayaka Missions University, India

Dr. Yong Li, School of Electronic and Information Engineering, Beijing Jiaotong University, P.R. China

Assist. Prof. Sugam Sharma, NIET, India/ Iowa State University, USA

Dr. Jorge A. Ruiz-Vanoye, Universidad Autonoma del Estado de Morelos, Mexico

Dr. Neeraj Kumar, SMVD University, Katra (J&K), India

Dr Genge Bela, "Petru Maior" University of Targu Mures, Romania

Dr. Junjie Peng, Shanghai University, P. R. China

Dr. Ilhem LENGLIZ, HANA Group - CRISTAL Laboratory, Tunisia

Prof. Dr. Durgesh Kumar Mishra, Acropolis Institute of Technology and Research, Indore, MP, India

Jorge L. Hernandez-Ardieta, University Carlos III of Madrid, Spain

Prof. Dr.C.Suresh Gnana Dhas, Anna University, India

Mrs Li Fang, Nanyang Technological University, Singapore

Prof. Pijush Biswas, RCC Institute of Information Technology, India

Dr. Siddhivinayak Kulkarni, University of Ballarat, Ballarat, Victoria, Australia

Dr. A. Arul Lawrence, Royal College of Engineering & Technology, India

Mr. Wongyos Keardsri, Chulalongkorn University, Bangkok, Thailand

Mr. Somesh Kumar Dewangan, CSVTU Bhilai (C.G.)/ Dimat Raipur, India

Mr. Hayder N. Jasem, University Putra Malaysia, Malaysia

Mr. A.V.Senthil Kumar, C. M. S. College of Science and Commerce, India

Mr. R. S. Karthik, C. M. S. College of Science and Commerce, India

Mr. P. Vasant, University Technology Petronas, Malaysia

Mr. Wong Kok Seng, Soongsil University, Seoul, South Korea

Mr. Praveen Ranjan Srivastava, BITS PILANI, India

Mr. Kong Sang Kelvin, Leong, The Hong Kong Polytechnic University, Hong Kong

Mr. Mohd Nazri Ismail, Universiti Kuala Lumpur, Malaysia

Dr. Rami J. Matarneh, Al-isra Private University, Amman, Jordan

Dr Ojesanmi Olusegun Ayodeji, Ajayi Crowther University, Oyo, Nigeria

Dr. Riktesh Srivastava, Skyline University, UAE

Dr. Oras F. Baker, UCSI University - Kuala Lumpur, Malaysia

Dr. Ahmed S. Ghiduk, Faculty of Science, Beni-Suef University, Egypt

and Department of Computer science, Taif University, Saudi Arabia

Mr. Tirthankar Gayen, NT Kharagpur, India

Ms. Huei-Ru Tseng, National Chiao Tung University, Taiwan

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Prof. Ning Xu, Wuhan University of Technology, China

Mr Mohammed Salem Binwahlan, Hadhramout University of Science and Technology, Yemen

& Universiti Teknologi Malaysia, Malaysia.

Dr. Aruna Ranganath, Bhoj Reddy Engineering College for Women, India

Mr. Hafeezullah Amin, Institute of Information Technology, KUST, Kohat, Pakistan

Prof. Syed S. Rizvi, University of Bridgeport, USA

Mr. Shahbaz Pervez Chattha, University of Engineering and Technology Taxila, Pakistan

Dr. Shishir Kumar, Jaypee University of Information Technology, Wakanaghat (HP), India

Mr. Shahid Mumtaz, Portugal Telecommunication, Instituto de Telecomunicagoes (IT) , Aveiro, Portugal

Mr. Rajesh K Shukla, Corporate Institute of Science & Technology Bhopal M P

Dr. Poonam Garg, Institute of Management Technology, India

Mr. S. Mehta, Inha University, Korea

Mr. Dilip Kumar S.M, University Visvesvaraya College of Engineering (UVCE), Bangalore University,

Bangalore

Prof. Malik Sikander Hayat Khiyal, Fatima Jinnah Women University, Rawalpindi, Pakistan

Dr. Virendra Gomase , Department of Bioinformatics, Padmashree Dr. D.Y. Patil University

Dr. Irraivan Elamvazuthi, University Technology PETRONAS, Malaysia

Mr. Saqib Saeed, University of Siegen, Germany

Mr. Pavan Kumar Gorakavi, IPMA-USA [YC]

Dr. Ahmed Nabih Zaki Rashed, Menoufia University, Egypt

Prof. Shishir K. Shandilya, Rukmani Devi Institute of Science & Technology, India

Mrs.J.Komala Lakshmi, SNR Sons College, Computer Science, India

Mr. Muhammad Sohail, KUST, Pakistan

Dr. Manjaiah D.H, Mangalore University, India

Dr. S Santhosh Baboo, D.G.Vaishnav College, Chennai, India

Prof. Dr. Mokhtar Beldjehem, Sainte-Anne University, Halifax, NS, Canada

Dr. Deepak Laxmi Narasimha, Faculty of Computer Science and Information Technology, University of

Malaya, Malaysia

Prof. Dr. Arunkumar Thangavelu, Vellore Institute Of Technology, India

Mr. M. Azath, Anna University, India

Mr. Md. Rabiul Islam, Rajshahi University of Engineering & Technology (RUET), Bangladesh

Mr. Aos Alaa Zaidan Ansaef, Multimedia University, Malaysia

Dr Suresh Jain, Professor (on leave), Institute of Engineering & Technology, Devi Ahilya University, Indore

(MP) India,

Dr. Mohammed M. Kadhum, Universiti Utara Malaysia

Mr. Hanumanthappa. J. University of Mysore, India

Mr. Syed Ishtiaque Ahmed, Bangladesh University of Engineering and Technology (BUET)

Mr Akinola Solomon Olalekan, University of Ibadan, Ibadan, Nigeria

Mr. Santosh K. Pandey, Department of Information Technology, The Institute of Chartered Accountants of

India

Dr. P. Vasant, Power Control Optimization, Malaysia

Dr. Petr Ivankov, Automatika - S, Russian Federation

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Dr. Utkarsh Seetha, Data Infosys Limited, India

Mrs. Priti Maheshwary, Maulana Azad National Institute of Technology, Bhopal

Dr. (Mrs) Padmavathi Ganapathi, Avinashilingam University for Women, Coimbatore

Assist. Prof. A. Neela madheswari, Anna university, India

Prof. Ganesan Ramachandra Rao, PSG College of Arts and Science, India

Mr. Kamanashis Biswas, Daffodil International University, Bangladesh

Dr. Atul Gonsai, Saurashtra University, Gujarat, India

Mr. Angkoon Phinyomark, Prince of Songkla University, Thailand

Mrs. G. Nalini Priya, Anna University, Chennai

Dr. P. Subashini, Avinashilingam University for Women, India

Assoc. Prof. Vijay Kumar Chakka, Dhirubhai Ambani IICT, Gandhinagar .Gujarat

Mr Jitendra Agrawal, : Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal

Mr. Vishal Goyal, Department of Computer Science, Punjabi University, India

Dr. R. Baskaran, Department of Computer Science and Engineering, Anna University, Chennai

Assist. Prof, Kanwalvir Singh Dhindsa, B.B.S.B.Engg. College, Fatehgarh Sahib (Punjab), India

Dr. Jamal Ahmad Dargham, School of Engineering and Information Technology, Universiti Malaysia Sabah

Mr. Nitin Bhatia, DAV College, India

Dr. Dhavachelvan Ponnurangam, Pondicherry Central University, India

Dr. Mohd Faizal Abdollah, University of Technical Malaysia, Malaysia

Assist. Prof. Sonal Chawla, Panjab University, India

Dr. Abdul Wahid, AKG Engg. College, Ghaziabad, India

Mr. Arash Habibi Lashkari, University of Malaya (UM), Malaysia

Mr. Md. Rajibul Islam, Ibnu Sina Institute, University Technology Malaysia

Professor Dr. Sabu M. Thampi, .B.S Institute of Technology for Women, Kerala University, India

Mr. Noor Muhammed Nayeem, Universite Lumiere Lyon 2, 69007 Lyon, France

Dr. Himanshu Aggarwal, Department of Computer Engineering, Punjabi University, India

Prof R. Naidoo, Dept of Mathematics/Center for Advanced Computer Modelling, Durban University of

Technology, Durban, South Africa

Prof. Mydhili K Nair, M S Ramaiah Institute of Technology(M. S.R.I. T), Affliliated to Visweswaraiah

Technological University, Bangalore, India

M. Prabu, Adhiyamaan College of Engineering/Anna University, India

Mr. Swakkhar Shatabda, Department of Computer Science and Engineering, United International University,

Bangladesh

Dr. Abdur Rashid Khan, ICIT, Gomal University, Dera Ismail Khan, Pakistan

Mr. H. Abdul Shabeer, l-Nautix Technologies, Chennai, India

Dr. M. Aramudhan, Perunthalaivar Kamarajar Institute of Engineering and Technology, India

Dr. M. P. Thapliyal, Department of Computer Science, HNB Garhwal University (Central University), India

Dr. Shahaboddin Shamshirband, Islamic Azad University, Iran

Mr. Zeashan Hameed Khan, : Universite de Grenoble, France

Prof. Anil K Ahlawat, Ajay Kumar Garg Engineering College, Ghaziabad, UP Technical University, Lucknow

Mr. Longe Olumide Babatope, University Of Ibadan, Nigeria

Associate Prof. Raman Maini, University College of Engineering, Punjabi University, India

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Dr. Maslin Masrom, University Technology Malaysia, Malaysia

Sudipta Chattopadhyay, Jadavpur University, Kolkata, India

Dr. Dang Tuan NGUYEN, University of Information Technology, Vietnam National University - Ho Chi Minh

City

Dr. Mary Lourde R., BITS-PILANI Dubai , UAE

Dr. Abdul Aziz, University of Central Punjab, Pakistan

Mr. Karan Singh, Gautam Budtha University, India

Mr. Avinash Pokhriyal, Uttar Pradesh Technical University, Lucknow, India

Associate Prof Dr Zuraini Ismail, University Technology Malaysia, Malaysia

Assistant Prof. Yasser M. Alginahi, College of Computer Science and Engineering, Taibah University,

Mad in ah Munawwarrah, KSA

Mr. Dakshina Ranjan Kisku, West Bengal University of Technology, India

Mr. Raman Kumar, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, India

Associate Prof. Samir B. Patel, Institute of Technology, Nirma University, India

Dr. M.Munir Ahamed Rabbani, B. S. Abdur Rahman University, India

Asst. Prof. Koushik Majumder, West Bengal University of Technology, India

Dr. Alex Pappachen James, Queensland Micro-nanotechnology center, Griffith University, Australia

Assistant Prof. S. Hariharan, B.S. Abdur Rahman University, India

Asst Prof. Jasmine. K. S, R.V. College of Engineering, India

Mr Naushad AN Mamode Khan, Ministry of Education and Human Resources, Mauritius

Prof. Mahesh Goyani, G H Patel Collge of Engg. & Tech, V.V.N, Anand, Gujarat, India

Dr. Mana Mohammed, University of Tlemcen, Algeria

Prof. Jatinder Singh, Universal Institutiion of Engg. & Tech. CHD, India

Mrs. M. Anandhavalli Gauthaman, Sikkim Manipal Institute of Technology, Majitar, East Sikkim

Dr. Bin Guo, Institute Telecom SudParis, France

Mrs. Maleika Mehr Nigar Mohamed Heenaye-Mamode Khan, University of Mauritius

Prof. Pijush Biswas, RCC Institute of Information Technology, India

Mr. V. Bala Dhandayuthapani, Mekelle University, Ethiopia

Dr. Irfan Syamsuddin, State Polytechnic of Ujung Pandang, Indonesia

Mr. Kavi Kumar Khedo, University of Mauritius, Mauritius

Mr. Ravi Chandiran, Zagro Singapore Pte Ltd. Singapore

Mr. Milindkumar V. Sarode, Jawaharlal Darda Institute of Engineering and Technology, India

Dr. Shamimul Qamar, KSJ Institute of Engineering & Technology, India

Dr. C. Arun, Anna University, India

Assist. Prof. M.N.Birje, Basaveshwar Engineering College, India

Prof. Hamid Reza Naji, Department of Computer Enigneering, Shahid Beheshti University, Tehran, Iran

Assist. Prof. Debasis Giri, Department of Computer Science and Engineering, Haldia Institute of Technology

Subhabrata Barman, Haldia Institute of Technology, West Bengal

Mr. M. I. Lali, COMSATS Institute of Information Technology, Islamabad, Pakistan

Dr. Feroz Khan, Central Institute of Medicinal and Aromatic Plants, Lucknow, India

Mr. R. Nagendran, Institute of Technology, Coimbatore, Tamilnadu, India

Mr. Amnach Khawne, King Mongkut's Institute of Technology Ladkrabang, Ladkrabang, Bangkok, Thailand

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Dr. P. Chakrabarti, Sir Padampat Singhania University, Udaipur, India

Mr. Nafiz Imtiaz Bin Hamid, Islamic University of Technology (IUT), Bangladesh.

Shahab-A. Shamshirband, Islamic Azad University, Chalous, Iran

Prof. B. Priestly Shan, Anna Univeristy, Tamilnadu, India

Venkatramreddy Velma, Dept. of Bioinformatics, University of Mississippi Medical Center, Jackson MS USA

Akshi Kumar, Dept. of Computer Engineering, Delhi Technological University, India

Dr. Umesh Kumar Singh, Vikram University, Ujjain, India

Mr. Serguei A. Mokhov, Concordia University, Canada

Mr. Lai Khin Wee, Universiti Teknologi Malaysia, Malaysia

Dr. Awadhesh Kumar Sharma, Madan Mohan Malviya Engineering College, India

Mr. Syed R. Rizvi, Analytical Services & Materials, Inc., USA

Dr. S. Karthik, SNS Collegeof Technology, India

Mr. Syed Qasim Bukhari, CIMET (Universidad de Granada), Spain

Mr. A.D.Potgantwar, Pune University, India

Dr. Himanshu Aggarwal, Punjabi University, India

Mr. Rajesh Ramachandran, Naipunya Institute of Management and Information Technology, India

Dr. K.L. Shunmuganathan, R.M.K Engg College , Kavaraipettai .Chennai

Dr. Prasant Kumar Pattnaik, KIST, India.

Dr. Ch. Aswani Kumar, VIT University, India

Mr. Ijaz AN Shoukat, King Saud University, Riyadh KSA

Mr. Arun Kumar, Sir Padam Pat Singhania University, Udaipur, Rajasthan

Mr. Muhammad Imran Khan, Universiti Teknologi PETRONAS, Malaysia

Dr. Natarajan Meghanathan, Jackson State University, Jackson, MS, USA

Mr. Mohd Zaki Bin Mas'ud, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia

Prof. Dr. R. Geetharamani, Dept. of Computer Science and Eng., Rajalakshmi Engineering College, India

Dr. Smita Rajpal, Institute of Technology and Management, Gurgaon, India

Dr. S. Abdul Khader Jilani, University of Tabuk, Tabuk, Saudi Arabia

Mr. Syed Jamal Haider Zaidi, Bahria University, Pakistan

Dr. N. Devarajan, Government College of Technology, Coimbatore, Tamilnadu, INDIA

Mr. R. Jagadeesh Kannan, RMK Engineering College, India

Mr. Deo Prakash, Shri Mata Vaishno Devi University, India

Mr. Mohammad Abu Naser, Dept. of EEE, IUT, Gazipur, Bangladesh

Assist. Prof. Prasun Ghosal, Bengal Engineering and Science University, India

Mr. Md. Golam Kaosar, School of Engineering and Science, Victoria University, Melbourne City, Australia

Mr. R. Mahammad Shafi, Madanapalle Institute of Technology & Science, India

Dr. F.Sagayaraj Francis, Pondicherry Engineering College, India

Dr. Ajay Goel, HIET , Kaithal, India

Mr. Nayak Sunil Kashibarao, Bahirji Smarak Mahavidyalaya, India

Mr. Suhas J Manangi, Microsoft India

Dr. Kalyankar N. V., Yeshwant Mahavidyalaya, Nanded , India

Dr. K.D. Verma, S.V. College of Post graduate studies & Research, India

Dr. Amjad Rehman, University Technology Malaysia, Malaysia

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Mr. Rachit Garg, L K College, Jalandhar, Punjab

Mr. J. William, M.A.M college of Engineering, Trichy, Tamilnadu, India

Prof. Jue-Sam Chou, Nanhua University, College of Science and Technology, Taiwan

Dr. Thorat S.B., Institute of Technology and Management, India

Mr. Ajay Prasad, Sir Padampat Singhania University, Udaipur, India

Dr. Kamaljit I. Lakhtaria, Atmiya Institute of Technology & Science, India

Mr. Syed Rafiul Hussain, Ahsanullah University of Science and Technology, Bangladesh

Mrs FazeelaTunnisa, Najran University, Kingdom of Saudi Arabia

Mrs Kavita Taneja, Maharishi Markandeshwar University, Haryana, India

Mr. Maniyar Shiraz Ahmed, Najran University, Najran, KSA

Mr. Anand Kumar, AMC Engineering College, Bangalore

Dr. Rakesh Chandra Gangwar, Beant College of Engg. & Tech., Gurdaspur (Punjab) India

Dr. V V Rama Prasad, Sree Vidyanikethan Engineering College, India

Assist. Prof. Neetesh Kumar Gupta, Technocrats Institute of Technology, Bhopal (M.P.), India

Mr. Ashish Seth, Uttar Pradesh Technical University, Lucknow ,UP India

Dr. V V S S S Balaram, Sreenidhi Institute of Science and Technology, India

Mr Rahul Bhatia, Lingaya's Institute of Management and Technology, India

Prof. Niranjan Reddy. P, KITS , Warangal, India

Prof. Rakesh. Lingappa, Vijetha Institute of Technology, Bangalore, India

Dr. Mohammed Ali Hussain, Nimra College of Engineering & Technology, Vijayawada, A. P., India

Dr. A.Srinivasan, MNM Jain Engineering College, Rajiv Gandhi Salai, Thorapakkam, Chennai

Mr. Rakesh Kumar, M.M. University, Mullana, Ambala, India

Dr. Lena Khaled, Zarqa Private University, Aman, Jordon

Ms. Supriya Kapoor, Patni/Lingaya's Institute of Management and Tech., India

Dr. Tossapon Boongoen , Aberystwyth University, UK

Dr . Bilal Alatas, Firat University, Turkey

Assist. Prof. Jyoti Praaksh Singh , Academy of Technology, India

Dr. Ritu Soni, GNG College, India

Dr . Mahendra Kumar , Sagar Institute of Research & Technology, Bhopal, India.

Dr. Binod Kumar, Lakshmi Narayan College of Tech.(LNCT)Bhopal India

Dr. Muzhir Shaban Al-Ani, Amman Arab University Amman - Jordan

Dr. T.C. Manjunath , ATRIA Institute of Tech, India

Mr. Muhammad Zakarya, COMSATS Institute of Information Technology (CUT), Pakistan

Assist. Prof. Harmunish Taneja, M. M. University, India

Dr. Chitra Dhawale , SICSR, Model Colony, Pune, India

Mrs Sankari Muthukaruppan, Nehru Institute of Engineering and Technology, Anna University, India

Mr. Aaqif Afzaal Abbasi, National University Of Sciences And Technology, Islamabad

Prof. Ashutosh Kumar Dubey, Trinity Institute of Technology and Research Bhopal, India

Mr. G. Appasami, Dr. Pauls Engineering College, India

Mr. M Yasin, National University of Science and Tech, karachi (NUST), Pakistan

Mr. Yaser Miaji, University Utara Malaysia, Malaysia

Mr. Shah Ahsanul Hague, International Islamic University Chittagong (IIUC), Bangladesh

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Prof. (Dr) Syed Abdul Sattar, Royal Institute of Technology & Science, India

Dr. S. Sasikumar, Roever Engineering College

Assist. Prof. Monit Kapoor, Maharishi Markandeshwar University, India

Mr. Nwaocha Vivian O, National Open University of Nigeria

Dr. M. S. Vijaya, GR Govindarajulu School of Applied Computer Technology, India

Assist. Prof. Chakresh Kumar, Manav Rachna International University, India

Mr. Kunal Chadha , R&D Software Engineer, Gemalto, Singapore

Mr. Mueen Uddin, Universiti Teknologi Malaysia, UTM , Malaysia

Dr. Dhuha Basheer abdullah, Mosul university, Iraq

Mr. S. Audithan, Annamalai University, India

Prof. Vijay K Chaudhari, Technocrats Institute of Technology , India

Associate Prof. Mohd llyas Khan, Technocrats Institute of Technology , India

Dr. Vu Thanh Nguyen, University of Information Technology, HoChiMinh City, VietNam

Assist. Prof. Anand Sharma, MITS, Lakshmangarh, Sikar, Rajasthan, India

Prof. T V Narayana Rao, HITAM Engineering college, Hyderabad

Mr. Deepak Gour, Sir Padampat Singhania University, India

Assist. Prof. Amutharaj Joyson, Kalasalingam University, India

Mr. Ali Balador, Islamic Azad University, Iran

Mr. Mohit Jain, Maharaja Surajmal Institute of Technology, India

Mr. Dilip Kumar Sharma, GLA Institute of Technology & Management, India

Dr. Debojyoti Mitra, Sir padampat Singhania University, India

Dr. Ali Dehghantanha, Asia-Pacific University College of Technology and Innovation, Malaysia

Mr. Zhao Zhang, City University of Hong Kong, China

Prof. S.P. Setty, A.U. College of Engineering, India

Prof. Patel Rakeshkumar Kantilal, Sankalchand Patel College of Engineering, India

Mr. Biswajit Bhowmik, Bengal College of Engineering & Technology, India

Mr. Manoj Gupta, Apex Institute of Engineering & Technology, India

Assist. Prof. Ajay Sharma, Raj Kumar Goel Institute Of Technology, India

Assist. Prof. Ramveer Singh, Raj Kumar Goel Institute of Technology, India

Dr. Hanan Elazhary, Electronics Research Institute, Egypt

Dr. Hosam I. Faiq, USM, Malaysia

Prof. Dipti D. Patil, MAEER's MIT College of Engg. & Tech, Pune, India

Assist. Prof. Devendra Chack, BCT Kumaon engineering College Dwarahat Almora, India

Prof. Manpreet Singh, M. M. Engg. College, M. M. University, India

Assist. Prof. M. Sadiq ali Khan, University of Karachi, Pakistan

Mr. Prasad S. Halgaonkar, MIT - College of Engineering, Pune, India

Dr. Imran Ghani, Universiti Teknologi Malaysia, Malaysia

Prof. Varun Kumar Kakar, Kumaon Engineering College, Dwarahat, India

Assist. Prof. Nisheeth Joshi, Apaji Institute, Banasthali University, Rajasthan, India

Associate Prof. Kunwar S. Vaisla, VCT Kumaon Engineering College, India

Prof Anupam Choudhary, Bhilai School Of Engg. .Bhilai (C.G.), India

Mr. Divya Prakash Shrivastava, Al Jabal Al garbi University, Zawya, Libya

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Associate Prof. Dr. V. Radha, Avinashilingam Deemed university for women, Coimbatore.

Dr. Kasarapu Ramani, JNT University, Anantapur, India

Dr. Anuraag Awasthi, Jayoti Vidyapeeth Womens University, India

Dr. C G Ravichandran, R V S College of Engineering and Technology, India

Dr. Mohamed A. Deriche, King Fahd University of Petroleum and Minerals, Saudi Arabia

Mr. Abbas Karimi, Universiti Putra Malaysia, Malaysia

Mr. Amit Kumar, Jaypee University of Engg. and Tech., India

Dr. Nikolai Stoianov, Defense Institute, Bulgaria

Assist. Prof. S. Ranichandra, KSR College of Arts and Science, Tiruchencode

Mr. T.K.P. Rajagopal, Diamond Horse International Pvt Ltd, India

Dr. Md. Ekramul Hamid, Rajshahi University, Bangladesh

Mr. Hemanta Kumar Kalita , TATA Consultancy Services (TCS), India

Dr. Messaouda Azzouzi, Ziane Achour University of Djelfa, Algeria

Prof. (Dr.) Juan Jose Martinez Castillo, "Gran Mariscal de Ayacucho" University and Acantelys research

Group, Venezuela

Dr. Jatinderkumar R. Saini, Narmada College of Computer Application, India

Dr. Babak Bashari Rad, University Technology of Malaysia, Malaysia

Dr. Nighat Mir, Effat University, Saudi Arabia

Prof. (Dr.) G.M.Nasira, Sasurie College of Engineering, India

Mr. Varun Mittal, Gemalto Pte Ltd, Singapore

Assist. Prof. Mrs P. Banumathi, Kathir College Of Engineering, Coimbatore

Assist. Prof. Quan Yuan, University of Wisconsin-Stevens Point, US

Dr. Pranam Paul, Narula Institute of Technology, Agarpara, West Bengal, India

Assist. Prof. J. Ramkumar, V.L.B Janakiammal college of Arts & Science, India

Mr. P. Sivakumar, Anna university, Chennai, India

Mr. Md. Humayun Kabir Biswas, King Khalid University, Kingdom of Saudi Arabia

Mr. Mayank Singh, J. P. Institute of Engg & Technology, Meerut, India

HJ. Kamaruzaman Jusoff, Universiti Putra Malaysia

Mr. Nikhil Patrick Lobo, CADES, India

Dr. Amit Wason, Rayat-Bahra Institute of Engineering & Boi-Technology, India

Dr. Rajesh Shrivastava, Govt. Benazir Science & Commerce College, Bhopal, India

Assist. Prof. Vishal Bharti, DCE, Gurgaon

Mrs. Sunita Bansal, Birla Institute of Technology & Science, India

Dr. R. Sudhakar, Dr.Mahalingam college of Engineering and Technology, India

Dr. Amit Kumar Garg, Shri Mata Vaishno Devi University, Katra(J&K), India

Assist. Prof. Raj Gaurang Tiwari, AZAD Institute of Engineering and Technology, India

Mr. Hamed Taherdoost, Tehran, Iran

Mr. Amin Daneshmand Malayeri, YRC, IAU, Malayer Branch, Iran

Mr. Shantanu Pal, University of Calcutta, India

Dr. Terry H. Walcott, E-Promag Consultancy Group, United Kingdom

Dr. Ezekiel U OKIKE, University of Ibadan, Nigeria

Mr. P. Mahalingam, Caledonian College of Engineering, Oman

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Dr. Mahmoud M. A. Abd Ellatif, Mansoura University, Egypt

Prof. Kunwar S. Vaisla, BCT Kumaon Engineering College, India

Prof. Mahesh H. Panchal, Kalol Institute of Technology & Research Centre, India

Mr. Muhammad Asad, Technical University of Munich, Germany

Mr. AliReza Shams Shafigh, Azad Islamic university, Iran

Prof. S. V. Nagaraj, RMK Engineering College, India

Mr. Ashikali M Hasan, Senior Researcher, CelNet security, India

Dr. Adnan Shahid Khan, University Technology Malaysia, Malaysia

Mr. Prakash Gajanan Burade, Nagpur University/ITM college of engg, Nagpur, India

Dr. Jagdish B.Helonde, Nagpur University/ITM college of engg, Nagpur, India

Professor, Doctor BOUHORMA Mohammed, Univertsity Abdelmalek Essaadi, Morocco

Mr. K. Thirumalaivasan, Pondicherry Engg. College, India

Mr. Umbarkar Anantkumar Janardan, Walchand College of Engineering, India

Mr. Ashish Chaurasia, Gyan Ganga Institute of Technology & Sciences, India

Mr. Sunil Taneja, Kurukshetra University, India

Mr. Fauzi Adi Rafrastara, Dian Nuswantoro University, Indonesia

Dr. Yaduvir Singh, Thapar University, India

Dr. loannis V. Koskosas, University of Western Macedonia, Greece

Dr. Vasantha Kalyani David, Avinashilingam University for women, Coimbatore

Dr. Ahmed Mansour Manasrah, Universiti Sains Malaysia, Malaysia

Miss. Nazanin Sadat Kazazi, University Technology Malaysia, Malaysia

Mr. Saeed Rasouli Heikalabad, Islamic Azad University - Tabriz Branch, Iran

Assoc. Prof. Dhirendra Mishra, SVKM's NMIMS University, India

Prof. Shapoor Zarei, UAE Inventors Association, UAE

Prof. B.Raja Sarath Kumar, Lenora College of Engineering, India

Dr. Bashir Alam, Jamia millia Islamia, Delhi, India

Prof. Anant J Umbarkar, Walchand College of Engg., India

Assist. Prof. B. Bharathi, Sathyabama University, India

Dr. Fokrul Alom Mazarbhuiya, King Khalid University, Saudi Arabia

Prof. T.S.Jeyali Laseeth, Anna University of Technology, Tirunelveli, India

Dr. M. Balraju, Jawahar Lai Nehru Technological University Hyderabad, India

Dr. Vijayalakshmi M. N., R.V. College of Engineering, Bangalore

Prof. Walid Moudani, Lebanese University, Lebanon

Dr. Saurabh Pal, VBS Purvanchal University, Jaunpur, India

Associate Prof. Suneet Chaudhary, Dehradun Institute of Technology, India

Associate Prof. Dr. Manuj Darbari, BBD University, India

Ms. Prema Selvaraj, K.S.R College of Arts and Science, India

Assist. Prof. Ms.S.Sasikala, KSR College of Arts & Science, India

Mr. Sukhvinder Singh Deora, NC Institute of Computer Sciences, India

Dr. Abhay Bansal, Amity School of Engineering & Technology, India

Ms. Sumita Mishra, Amity School of Engineering and Technology, India

Professor S. Viswanadha Raju, JNT University Hyderabad, India

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Mr. Asghar Shahrzad Khashandarag, Islamic Azad University Tabriz Branch, India

Mr. Manoj Sharma, Panipat Institute of Engg. & Technology, India

Mr. Shakeel Ahmed, King Faisal University, Saudi Arabia

Dr. Mohamed Ali Mahjoub, Institute of Engineer of Monastir, Tunisia

Mr. Adri Jovin J.J., SriGuru Institute of Technology, India

Dr. Sukumar Senthilkumar, Universiti Sains Malaysia, Malaysia

Mr. Rakesh Bharati, Dehradun Institute of Technology Dehradun, India

Mr. Shervan Fekri Ershad, Shiraz International University, Iran

Mr. Md. Safiqul Islam, Daffodil International University, Bangladesh

Mr. Mahmudul Hasan, Daffodil International University, Bangladesh

Prof. Mandakini Tayade, UIT, RGTU, Bhopal, India

Ms. Sarla More, UIT, RGTU, Bhopal, India

Mr. Tushar Hrishikesh Jaware, R.C. Patel Institute of Technology, Shirpur, India

Ms. C. Divya, Dr G R Damodaran College of Science, Coimbatore, India

Mr. Fahimuddin Shaik, Annamacharya Institute of Technology & Sciences, India

Dr. M. N. Giri Prasad, JNTUCE.Pulivendula, A.P., India

Assist. Prof. Chintan M Bhatt, Charotar University of Science And Technology, India

Prof. Sahista Machchhar, Marwadi Education Foundation's Group of institutions, India

Assist. Prof. Navnish Goel, S. D. College Of Enginnering & Technology, India

Mr. Khaja Kamaluddin, Sirt University, Sirt, Libya

Mr. Mohammad Zaidul Karim, Daffodil International, Bangladesh

Mr. M. Vijayakumar, KSR College of Engineering, Tiruchengode, India

Mr. S. A. Ahsan Rajon, Khulna University, Bangladesh

Dr. Muhammad Mohsin Nazir, LCW University Lahore, Pakistan

Mr. Mohammad Asadul Hoque, University of Alabama, USA

Mr. P.V.Sarathchand, Indur Institute of Engineering and Technology, India

Mr. Durgesh Samadhiya, Chung Hua University, Taiwan

DrVenu Kuthadi, University of Johannesburg, Johannesburg, RSA

Dr. (Er) Jasvir Singh, Guru Nanak Dev University, Amritsar, Punjab, India

Mr. Jasmin Cosic, Min. of the Interior of Una-sana canton, B&H, Bosnia and Herzegovina

Dr S. Rajalakshmi, Botho College, South Africa

Dr. Mohamed Sarrab, De Montfort University, UK

Mr. Basappa B. Kodada, Canara Engineering College, India

Assist. Prof. K. Ramana, Annamacharya Institute of Technology and Sciences, India

Dr. Ashu Gupta, Apeejay Institute of Management, Jalandhar, India

Assist. Prof. Shaik Rasool, Shadan College of Engineering & Technology, India

Assist. Prof. K. Suresh, Annamacharya Institute of Tech & Sci. Rajampet, AP, India

Dr . G. Singaravel, K.S.R. College of Engineering, India

Dr B. G. Geetha, K.S.R. College of Engineering, India

Assist. Prof. Kavita Choudhary, ITM University, Gurgaon

Dr. Mehrdad Jalali, Azad University, Mashhad, Iran

Megha Goel, Shamli Institute of Engineering and Technology, Shamli, India

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Mr. Chi-Hua Chen, Institute of Information Management, National Chiao-Tung University, Taiwan (R.O.C.)

Assoc. Prof. A. Rajendran, RVS College of Engineering and Technology, India

Assist. Prof. S. Jaganathan, RVS College of Engineering and Technology, India

Assoc. Prof. A S N Chakravarthy, Sri Aditya Engineering College, India

Assist. Prof. Deepshikha Patel, Technocrat Institute of Technology, India

Assist. Prof. Maram Balajee, GMRIT, India

Assist. Prof. Monika Bhatnagar, TIT, India

Prof. Gaurang Panchal, Charotar University of Science & Technology, India

Prof. Anand K. Tripathi, Computer Society of India

Prof. Jyoti Chaudhary, High Performance Computing Research Lab, India

Assist. Prof. Supriya Raheja, ITM University, India

Dr. Pankaj Gupta, Microsoft Corporation, U.S.A.

Assist. Prof. Panchamukesh Chandaka, Hyderabad Institute of Tech. & Management, India

Prof. Mohan H.S, SJB Institute Of Technology, India

Mr. Hossein Malekinezhad, Islamic Azad University, Iran

Mr. Zatin Gupta, Universti Malaysia, Malaysia

Assist. Prof. Amit Chauhan, Phonics Group of Institutions, India

Assist. Prof. Ajal A. J., METS School Of Engineering, India

Mrs. Omowunmi Omobola Adeyemo, University of Ibadan, Nigeria

Dr. Bharat Bhushan Agarwal, I.F.T.M. University, India

Md. Nazrul Islam, University of Western Ontario, Canada

Tushar Kanti, L.N.C.T, Bhopal, India

Er. Aumreesh Kumar Saxena, SIRTs College Bhopal, India

Mr. Mohammad Monirul Islam, Daffodil International University, Bangladesh

Dr. Kashif Nisar, University Utara Malaysia, Malaysia

Dr. Wei Zheng, Rutgers Univ/ A10 Networks, USA

Associate Prof. Rituraj Jain, Vyas Institute of Engg & Tech, Jodhpur - Rajasthan

Assist. Prof. Apoorvi Sood, I.T.M. University, India

Dr. Kayhan Zrar Ghafoor, University Technology Malaysia, Malaysia

Mr. Swapnil Soner, Truba Institute College of Engineering & Technology, Indore, India

Ms. Yogita Gigras, I.T.M. University, India

Associate Prof. Neelima Sadineni, Pydha Engineering College, India Pydha Engineering College

Assist. Prof. K. Deepika Rani, HITAM, Hyderabad

Ms. Shikha Maheshwari, Jaipur Engineering College & Research Centre, India

Prof. Dr V S Giridhar Akula, Avanthi's Scientific Tech. & Research Academy, Hyderabad

Prof. Dr.S.Saravanan, Muthayammal Engineering College, India

Mr. Mehdi Golsorkhatabar Amiri, Islamic Azad University, Iran

Prof. Amit Sadanand Savyanavar, MITCOE, Pune, India

Assist. Prof. P.Oliver Jayaprakash, Anna University.Chennai

Assist. Prof. Ms. Sujata, ITM University, Gurgaon, India

Dr. Asoke Nath, St. Xavier's College, India

Mr. Masoud Rafighi, Islamic Azad University, Iran

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Assist. Prof. RamBabu Pemula, NIMRA College of Engineering & Technology, India

Assist. Prof. Ms Rita Chhikara, ITM University, Gurgaon, India

Mr. Sandeep Maan, Government Post Graduate College, India

Prof. Dr. S. Muralidharan, Mepco Schlenk Engineering College, India

Associate Prof. T.V.Sai Krishna, QIS College of Engineering and Technology, India

Mr. R. Balu, Bharathiar University, Coimbatore, India

Assist. Prof. Shekhar. R, Dr.SM College of Engineering, India

Prof. P. Senthilkumar, Vivekanandha Institue of Engineering And Techology For Woman, India

Mr. M. Kamarajan, PSNA College of Engineering & Technology, India

Dr. Angajala Srinivasa Rao, Jawaharlal Nehru Technical University, India

Assist. Prof. C. Venkatesh, A.I.T.S, Rajampet, India

Mr. Afshin Rezakhani Roozbahani, Ayatollah Boroujerdi University, Iran

Mr. Laxmi chand, SCTL, Noida, India

Dr. Dr. Abdul Hannan, Vivekanand College, Aurangabad

Prof. Mahesh Panchal, KITRC, Gujarat

Dr. A. Subramani, K.S.R. College of Engineering, Tiruchengode

Assist. Prof. Prakash M, Rajalakshmi Engineering College, Chennai, India

Assist. Prof. Akhilesh K Sharma, Sir Padampat Singhania University, India

Ms. Varsha Sahni, Guru Nanak Dev Engineering College, Ludhiana, India

Associate Prof. Trilochan Rout, NM Institute Of Engineering And Technlogy, India

Mr. Srikanta Kumar Mohapatra, NMIET, Orissa, India

Mr. Waqas Haider Bangyal, Iqra University Islamabad, Pakistan

Dr. S. Vijayaragavan, Christ College of Engineering and Technology, Pondicherry, India

Prof. Elboukhari Mohamed, University Mohammed First, Oujda, Morocco

Dr. Muhammad Asif Khan, King Faisal University, Saudi Arabia

Dr. Nagy Ramadan Darwish Omran, Cairo University, Egypt.

Assistant Prof. Anand Nayyar, KCL Institute of Management and Technology, India

Mr. G. Premsankar, Ericcson, India

Assist. Prof. T. Hemalatha, VELS University, India

Prof. Tejaswini Apte, University of Pune, India

Dr. Edmund Ng Giap Weng, Universiti Malaysia Sarawak, Malaysia

Mr. Mahdi Nouri, Iran University of Science and Technology, Iran

Associate Prof. S. Asif Hussain, Annamacharya Institute of technology & Sciences, India

Mrs. Kavita Pabreja, Maharaja Surajmal Institute (an affiliate of GGSIP University), India

Mr. Vorugunti Chandra Sekhar, DA-IICT, India

Mr. Muhammad Najmi Ahmad Zabidi, Universiti Teknologi Malaysia, Malaysia

Dr. Aderemi A. Atayero, Covenant University, Nigeria

Assist. Prof. Osama Sohaib, Balochistan University of Information Technology, Pakistan

Assist. Prof. K. Suresh, Annamacharya Institute of Technology and Sciences, India

Mr. Hassen Mohammed Abduallah Alsafi, International Islamic University Malaysia (MUM) Malaysia

Mr. Robail Yasrab, Virtual University of Pakistan, Pakistan

Mr. R. Balu, Bharathiar University, Coimbatore, India

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 10, No. 6, June 2012

Prof. Anand Nayyar, KCL Institute of Management and Technology, Jalandhar

Assoc. Prof. Vivek S Deshpande, MIT College of Engineering, India

Prof. K. Saravanan, Anna university Coimbatore, India

Dr. Ravendra Singh, MJP Rohilkhand University, Bareilly, India

Mr. V. Mathivanan, IBRA College of Technology, Sultanate of OMAN

Assoc. Prof. S. Asif Hussain, AITS, India

Assist. Prof. C. Venkatesh, AITS, India

Mr. Sami Ulhaq, SZABIST Islamabad, Pakistan

Dr. B. Justus Rabi, Institute of Science & Technology, India

Mr. Anuj Kumar Yadav, Dehradun Institute of technology, India

Mr. Alejandro Mosquera, University of Alicante, Spain

Assist. Prof. Arjun Singh, Sir Padampat Singhania University (SPSU), Udaipur, India

Dr. Smriti Agrawal, JB Institute of Engineering and Technology, Hyderabad

Assist. Prof. Swathi Sambangi, Visakha Institute of Engineering and Technology, India

Ms. Prabhjot Kaur, Guru Gobind Singh Indraprastha University, India

Mrs. Samaher AL-Hothali, Yanbu University College, Saudi Arabia

Prof. Rajneeshkaur Bedi, MIT College of Engineering, Pune, India

Mr. Hassen Mohammed Abduallah Alsafi, International Islamic University Malaysia (HUM)

Dr. Wei Zhang, Amazon.com, Seattle, WA, USA

Mr. B. Santhosh Kumar, C S I College of Engineering, Tamil Nadu

Dr. K. Reji Kumar, , N S S College, Pandalam, India

Assoc. Prof. K. Seshadri Sastry, EIILM University, India

Mr. Kai Pan, UNC Charlotte, USA

Mr. Ruikar Sachin, SGGSIET, India

Prof. (Dr.) Vinodani Katiyar, Sri Ramswaroop Memorial University, India

Assoc. Prof., M. Giri, Sreenivasa Institute of Technology and Management Studies, India

Assoc. Prof. Labib Francis Gergis, Misr Academy for Engineering and Technology ( MET ), Egypt

Assist. Prof. Amanpreet Kaur, ITM University, India

Assist. Prof. Anand Singh Rajawat, Shri Vaishnav Institute of Technology & Science, Indore

Mrs. Hadeel Saleh Haj Aliwi, Universiti Sains Malaysia (USM), Malaysia

Dr. Abhay Bansal, Amity University, India

Dr. Mohammad A. Mezher, Fahad Bin Sultan University, KSA

Assist. Prof. Nidhi Arora, M.C.A. Institute, India

Prof. Dr. P. Suresh, Karpagam College of Engineering, Coimbatore, India

Dr. Kannan Balasubramanian, Mepco Schlenk Engineering College, India

Dr. S. Sankara Gomathi, Panimalar Engineering college, India

Prof. Anil kumar Suthar, Gujarat Technological University, L.C. Institute of Technology, India

Assist. Prof. R. Hubert Rajan, NOORUL ISLAM UNIVERSITY, India

Assist. Prof. Dr. Jyoti Mahajan, College of Engineering & Technology

Assist. Prof. Homam Reda El-Taj, College of Network Engineering, Saudi Arabia & Malaysia

Mr. Bijan Paul, Shahjalal University of Science & Technology, Bangladesh

Assoc. Prof. Dr. Ch V Phani Krishna, KL University, India

CALL FOR PAPERS
International Journal of Computer Science and Information Security

January - December
IJCSIS 2012

ISSN: 1947-5500

http://sites.google.com/site/ijcsis/

International Journal Computer Science and Information Security, IJCSIS, is the premier
scholarly venue in the areas of computer science and security issues. IJCSIS 2011 will provide a high
profile, leading edge platform for researchers and engineers alike to publish state-of-the-art research in the
respective fields of information technology and communication security. The journal will feature a diverse
mixture of publication articles including core and applied computer science related topics.

Authors are solicited to contribute to the special issue by submitting articles that illustrate research results,
projects, surveying works and industrial experiences that describe significant advances in the following
areas, but are not limited to. Submissions may span a broad range of topics, e.g.:

Track A: Security

Access control, Anonymity, Audit and audit reduction & Authentication and authorization, Applied
cryptography, Cryptanalysis, Digital Signatures, Biometric security, Boundary control devices,
Certification and accreditation, Cross-layer design for security, Security & Network Management, Data and
system integrity, Database security, Defensive information warfare, Denial of service protection, Intrusion
Detection, Anti-malware, Distributed systems security, Electronic commerce, E-mail security, Spam,
Phishing, E-mail fraud, Virus, worms, Trojan Protection, Grid security, Information hiding and
watermarking & Information survivability, Insider threat protection, Integrity

Intellectual property protection, Internet/Intranet Security, Key management and key recovery, Language-
based security, Mobile and wireless security, Mobile, Ad Hoc and Sensor Network Security, Monitoring
and surveillance, Multimedia security .Operating system security, Peer-to-peer security, Performance
Evaluations of Protocols & Security Application, Privacy and data protection, Product evaluation criteria
and compliance, Risk evaluation and security certification, Risk/vulnerability assessment, Security &
Network Management, Security Models & protocols, Security threats & countermeasures (DDoS, MiM,
Session Hijacking, Replay attack etc,), Trusted computing, Ubiquitous Computing Security, Virtualization
security, VoIP security, Web 2.0 security, Submission Procedures, Active Defense Systems, Adaptive
Defense Systems, Benchmark, Analysis and Evaluation of Security Systems, Distributed Access Control
and Trust Management, Distributed Attack Systems and Mechanisms, Distributed Intrusion
Detection/Prevention Systems, Denial-of-Service Attacks and Countermeasures, High Performance
Security Systems, Identity Management and Authentication, Implementation, Deployment and
Management of Security Systems, Intelligent Defense Systems, Internet and Network Forensics, Large-
scale Attacks and Defense, RFID Security and Privacy, Security Architectures in Distributed Network
Systems, Security for Critical Infrastructures, Security for P2P systems and Grid Systems, Security in E-
Commerce, Security and Privacy in Wireless Networks, Secure Mobile Agents and Mobile Code, Security
Protocols, Security Simulation and Tools, Security Theory and Tools, Standards and Assurance Methods,
Trusted Computing, Viruses, Worms, and Other Malicious Code, World Wide Web Security, Novel and
emerging secure architecture, Study of attack strategies, attack modeling, Case studies and analysis of
actual attacks, Continuity of Operations during an attack, Key management, Trust management, Intrusion
detection techniques, Intrusion response, alarm management, and correlation analysis, Study of tradeoffs
between security and system performance, Intrusion tolerance systems, Secure protocols, Security in
wireless networks (e.g. mesh networks, sensor networks, etc.), Cryptography and Secure Communications,
Computer Forensics, Recovery and Healing, Security Visualization, Formal Methods in Security, Principles
for Designing a Secure Computing System, Autonomic Security, Internet Security, Security in Health Care
Systems, Security Solutions Using Reconfigurable Computing, Adaptive and Intelligent Defense Systems,
Authentication and Access control, Denial of service attacks and countermeasures, Identity, Route and

Location Anonymity schemes, Intrusion detection and prevention techniques, Cryptography, encryption
algorithms and Key management schemes, Secure routing schemes, Secure neighbor discovery and
localization, Trust establishment and maintenance, Confidentiality and data integrity, Security architectures,
deployments and solutions, Emerging threats to cloud-based services, Security model for new services,
Cloud-aware web service security, Information hiding in Cloud Computing, Securing distributed data
storage in cloud, Security, privacy and trust in mobile computing systems and applications, Middleware
security & Security features: middleware software is an asset on

its own and has to be protected, interaction between security-specific and other middleware features, e.g.,
context-awareness, Middleware-level security monitoring and measurement: metrics and mechanisms
for quantification and evaluation of security enforced by the middleware, Security co-design: trade-off and
co-design between application-based and middleware-based security, Policy-based management:
innovative support for policy-based definition and enforcement of security concerns, Identification and
authentication mechanisms: Means to capture application specific constraints in defining and enforcing
access control rules, Middleware-oriented security patterns: identification of patterns for sound, reusable
security, Security in aspect-based middleware: mechanisms for isolating and enforcing security aspects,
Security in agent-based platforms: protection for mobile code and platforms, Smart Devices: Biometrics,
National ID cards, Embedded Systems Security and TPMs, RFID Systems Security, Smart Card Security,
Pervasive Systems: Digital Rights Management (DRM) in pervasive environments, Intrusion Detection and
Information Filtering, Localization Systems Security (Tracking of People and Goods), Mobile Commerce
Security, Privacy Enhancing Technologies, Security Protocols (for Identification and Authentication,
Confidentiality and Privacy, and Integrity), Ubiquitous Networks: Ad Hoc Networks Security, Delay-
Tolerant Network Security, Domestic Network Security, Peer-to-Peer Networks Security, Security Issues
in Mobile and Ubiquitous Networks, Security of GSM/GPRS/UMTS Systems, Sensor Networks Security,
Vehicular Network Security, Wireless Communication Security: Bluetooth, NFC, WiFi, WiMAX,
WiMedia, others

This Track will emphasize the design, implementation, management and applications of computer
communications, networks and services. Topics of mostly theoretical nature are also welcome, provided
there is clear practical potential in applying the results of such work.

Track B: Computer Science

Broadband wireless technologies: LTE, WiMAX, WiRAN, HSDPA, HSUPA, Resource allocation and
interference management, Quality of service and scheduling methods, Capacity planning and dimensioning,
Cross-layer design and Physical layer based issue, Interworking architecture and interoperability, Relay
assisted and cooperative communications, Location and provisioning and mobility management, Call
admission and flow/congestion control, Performance optimization, Channel capacity modeling and analysis,
Middleware Issues: Event-based, publish/subscribe, and message-oriented middleware, Reconfigurable,
adaptable, and reflective middleware approaches, Middleware solutions for reliability, fault tolerance, and
quality-of-service, Scalability of middleware, Context-aware middleware, Autonomic and self-managing
middleware, Evaluation techniques for middleware solutions, Formal methods and tools for designing,
verifying, and evaluating, middleware, Software engineering techniques for middleware, Service oriented
middleware, Agent-based middleware, Security middleware, Network Applications: Network-based
automation, Cloud applications, Ubiquitous and pervasive applications, Collaborative applications, RFID
and sensor network applications, Mobile applications, Smart home applications, Infrastructure monitoring
and control applications, Remote health monitoring, GPS and location-based applications, Networked
vehicles applications, Alert applications, Embeded Computer System, Advanced Control Systems, and
Intelligent Control : Advanced control and measurement, computer and microprocessor-based control,
signal processing, estimation and identification techniques, application specific IC's, nonlinear and
adaptive control, optimal and robot control, intelligent control, evolutionary computing, and intelligent
systems, instrumentation subject to critical conditions, automotive, marine and aero-space control and all
other control applications, Intelligent Control System, Wiring/Wireless Sensor, Signal Control System.
Sensors, Actuators and Systems Integration : Intelligent sensors and actuators, multisensor fusion, sensor
array and multi-channel processing, micro/nano technology, microsensors and microactuators,
instrumentation electronics, MEMS and system integration, wireless sensor, Network Sensor, Hybrid

Sensor, Distributed Sensor Networks. Signal and Image Processing : Digital signal processing theory,
methods, DSP implementation, speech processing, image and multidimensional signal processing, Image
analysis and processing, Image and Multimedia applications, Real-time multimedia signal processing,
Computer vision, Emerging signal processing areas, Remote Sensing, Signal processing in education.
Industrial Informatics: Industrial applications of neural networks, fuzzy algorithms, Neuro-Fuzzy
application, biolnformatics, real-time computer control, real-time information systems, human-machine
interfaces, CAD/CAM/CAT/CIM, virtual reality, industrial communications, flexible manufacturing
systems, industrial automated process, Data Storage Management, Harddisk control, Supply Chain
Management, Logistics applications, Power plant automation, Drives automation. Information Technology,
Management of Information System : Management information systems, Information Management,
Nursing information management, Information System, Information Technology and their application, Data
retrieval, Data Base Management, Decision analysis methods, Information processing, Operations research,
E-Business, E-Commerce, E-Government, Computer Business, Security and risk management, Medical
imaging, Biotechnology, Bio-Medicine, Computer-based information systems in health care, Changing
Access to Patient Information, Healthcare Management Information Technology.
Communication/Computer Network, Transportation Application : On-board diagnostics, Active safety
systems, Communication systems, Wireless technology, Communication application, Navigation and
Guidance, Vision-based applications, Speech interface, Sensor fusion, Networking theory and technologies,
Transportation information, Autonomous vehicle, Vehicle application of affective computing, Advance
Computing technology and their application : Broadband and intelligent networks, Data Mining, Data
fusion, Computational intelligence, Information and data security, Information indexing and retrieval,
Information processing, Information systems and applications, Internet applications and performances,
Knowledge based systems, Knowledge management, Software Engineering, Decision making, Mobile
networks and services, Network management and services, Neural Network, Fuzzy logics, Neuro-Fuzzy,
Expert approaches, Innovation Technology and Management : Innovation and product development,
Emerging advances in business and its applications, Creativity in Internet management and retailing, B2B
and B2C management, Electronic transceiver device for Retail Marketing Industries, Facilities planning
and management, Innovative pervasive computing applications, Programming paradigms for pervasive
systems, Software evolution and maintenance in pervasive systems, Middleware services and agent
technologies, Adaptive, autonomic and context-aware computing, Mobile/Wireless computing systems and
services in pervasive computing, Energy-efficient and green pervasive computing, Communication
architectures for pervasive computing, Ad hoc networks for pervasive communications, Pervasive
opportunistic communications and applications, Enabling technologies for pervasive systems (e.g., wireless
BAN, PAN), Positioning and tracking technologies, Sensors and RFID in pervasive systems, Multimodal
sensing and context for pervasive applications, Pervasive sensing, perception and semantic interpretation,
Smart devices and intelligent environments, Trust, security and privacy issues in pervasive systems, User
interfaces and interaction models, Virtual immersive communications, Wearable computers, Standards and
interfaces for pervasive computing environments, Social and economic models for pervasive systems,
Active and Programmable Networks, Ad Hoc & Sensor Network, Congestion and/or Flow Control, Content
Distribution, Grid Networking, High-speed Network Architectures, Internet Services and Applications,
Optical Networks, Mobile and Wireless Networks, Network Modeling and Simulation, Multicast,
Multimedia Communications, Network Control and Management, Network Protocols, Network
Performance, Network Measurement, Peer to Peer and Overlay Networks, Quality of Service and Quality
of Experience, Ubiquitous Networks, Crosscutting Themes - Internet Technologies, Infrastructure,
Services and Applications; Open Source Tools, Open Models and Architectures; Security, Privacy and
Trust; Navigation Systems, Location Based Services; Social Networks and Online Communities; ICT
Convergence, Digital Economy and Digital Divide, Neural Networks, Pattern Recognition, Computer
Vision, Advanced Computing Architectures and New Programming Models, Visualization and Virtual
Reality as Applied to Computational Science, Computer Architecture and Embedded Systems, Technology
in Education, Theoretical Computer Science, Computing Ethics, Computing Practices & Applications

Authors are invited to submit papers through e-mail ijcsiseditor(5)gmail.com . Submissions must be original
and should not have been published previously or be under consideration for publication while being
evaluated by IJCSIS. Before submission authors should carefully read over the journal's Author Guidelines,
which are located at http://sites.google.com/site/ijcsis/authors-notes .

IJCSIS PUBLICATION 2012
ISSN 1947 5500

Internet Archive Audio

Featured

Top

Images

Featured

Top

Software

Featured

Top

Books

Featured

Top

Video

Featured

Top

Mobile Apps

Browser Extensions

Archive-It Subscription

Save Page Now

Full text of "International Journal for Computer Science Research"

See other formats