International Journal of Research in Social Sciences (ISSN: 2249-2496)
CONTENTS
Sr.
No. TITLE & NAME OF THE AUTHOR (S)
Page
No.
1 Influential Factors Effect to the Trends of Online News Business.
Teetima Piyasirisilp and Chanchai Bunchapattanasukdav 1-19
2 Small and Medium Scale Enterprises and Economic Growth in Nigeria.
Obasan Kehinde A. and Adediran, Olanrewaju Adewole 20-46
3 Need and Importance of Professional Growth.
Dr. M. Sugunatha Reddy and Dr. B. Rama Bhupal Reddy 47-56
4 Non-formal Education in Punjab: Approaches for Lifelong Learning.
Zarabi Dazy and Nair Balakrishan 57-78
5
Effective Approach of Web Based Human Resource Management System for Optimistic
Development of Institutions.
Akmal Rehan, Nayyar Iqbal and Muhammad Milhan Afzal Khan
79-96
6 Food Security- Need for a Commitment to Universal Distribution System.
Dr. G. V. Kokkalki 97-105
7 Meaning and Determinants of Values: Research Insights.
Dr. S.L. Kaushal and Dr. Yasmin Janjhua 106-124
8 Consumer’s Expectation from Pharmaceutical Companies in Pune City.
Richa Arora and Nitin R. Mahankale 125-146
9 Rural Industrialization through Khadi and Village Industries: a Study.
M. Rama Satyanarayana and Ch. V. Krishna Reddy 147-160
10 The Socio-economic Characteristics of the Poor in Rural Kerala.
K. Rajan 161-189
11 Various Techniques in Intrusion Detection: A Survey.
Jay Kant Pratap Singh and Abhishek Kumar Sahu 190-204
12 Growth of Services Sector Under the WTO: An Analysis.
Dr. Jamil Ahmad 205-225
13 Dominance of Innovative Leadership in Construction Project Management.
S. Binil Sundar 226-254
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
Chief Patron Dr. JOSE G. VARGAS-HERNANDEZ
Member of the National System of Researchers, Mexico
Research professor at University Center of Economic and Managerial Sciences,
University of Guadalajara
Director of Mass Media at Ayuntamiento de Cd. Guzman
Ex. director of Centro de Capacitacion y Adiestramiento
Patron Dr. Mohammad Reza Noruzi
PhD: Public Administration, Public Sector Policy Making Management,
Tarbiat Modarres University, Tehran, Iran
Faculty of Economics and Management, Tarbiat Modarres University, Tehran, Iran
Young Researchers' Club Member, Islamic Azad University, Bonab, Iran
Chief Advisors Dr. NAGENDRA. S. Senior Asst. Professor,
Department of MBA, Mangalore Institute of Technology and Engineering, Moodabidri
Dr. SUNIL KUMAR MISHRA Associate Professor,
Dronacharya College of Engineering, Gurgaon, INDIA
Mr. GARRY TAN WEI HAN Lecturer and Chairperson (Centre for Business and Management),
Department of Marketing, University Tunku Abdul Rahman, MALAYSIA
MS. R. KAVITHA
Assistant Professor,
Aloysius Institute of Management and Information, Mangalore, INDIA
Dr. A. JUSTIN DIRAVIAM
Assistant Professor,
Dept. of Computer Science and Engineering, Sardar Raja College of Engineering,
Alangulam Tirunelveli, TAMIL NADU, INDIA
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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November
2011
Editorial Board
Dr. CRAIG E. REESE Professor, School of Business, St. Thomas University, Miami Gardens
Dr. S. N. TAKALIKAR Principal, St. Johns Institute of Engineering, PALGHAR (M.S.)
Dr. RAMPRATAP SINGH Professor, Bangalore Institute of International Management, KARNATAKA
Dr. P. MALYADRI Principal, Government Degree College, Osmania University, TANDUR
Dr. Y. LOKESWARA CHOUDARY Asst. Professor Cum, SRM B-School, SRM University, CHENNAI
Prof. Dr. TEKI SURAYYA Professor, Adikavi Nannaya University, ANDHRA PRADESH, INDIA
Dr. T. DULABABU Principal, The Oxford College of Business Management, BANGALORE
Dr. A. ARUL LAWRENCE SELVAKUMAR Professor, Adhiparasakthi Engineering College, MELMARAVATHUR, TN
Dr. S. D. SURYAWANSHI
Lecturer, College of Engineering Pune, SHIVAJINAGAR
Dr. S. KALIYAMOORTHY Professor & Director, Alagappa Institute of Management, KARAIKUDI
Prof S. R. BADRINARAYAN
Sinhgad Institute for Management & Computer Applications, PUNE
Mr. GURSEL ILIPINAR ESADE Business School, Department of Marketing, SPAIN
Mr. ZEESHAN AHMED Software Research Eng, Department of Bioinformatics, GERMANY
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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November
2011
Mr. SANJAY ASATI Dept of ME, M. Patel Institute of Engg. & Tech., GONDIA(M.S.)
Mr. G. Y. KUDALE N.M.D. College of Management and Research, GONDIA(M.S.)
Editorial Advisory Board
Dr. MANJIT DAS Assistant Professor, Deptt. of Economics, M.C.College, ASSAM
Dr. ROLI PRADHAN Maulana Azad National Institute of Technology, BHOPAL
Dr. N. KAVITHA Assistant Professor, Department of Management, Mekelle University, ETHIOPIA
Prof C. M. MARAN Assistant Professor (Senior), VIT Business School, TAMIL NADU
Dr. RAJIV KHOSLA Associate Professor and Head, Chandigarh Business School, MOHALI
Dr. S. K. SINGH Asst. Professor, R. D. Foundation Group of Institutions, MODINAGAR
Dr. (Mrs.) MANISHA N. PALIWAL Associate Professor, Sinhgad Institute of Management, PUNE
Dr. (Mrs.) ARCHANA ARJUN GHATULE Director, SPSPM, SKN Sinhgad Business School, MAHARASHTRA
Dr. NEELAM RANI DHANDA Associate Professor, Department of Commerce, kuk, HARYANA
Dr. FARAH NAAZ GAURI Associate Professor, Department of Commerce, Dr. Babasaheb Ambedkar Marathwada
University, AURANGABAD
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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November
2011
Prof. Dr. BADAR ALAM IQBAL Associate Professor, Department of Commerce, Aligarh Muslim University, UP
Dr. CH. JAYASANKARAPRASAD Assistant Professor, Dept. of Business Management, Krishna University, A. P., INDIA
Associate Editors
Dr. SANJAY J. BHAYANI Associate Professor ,Department of Business Management, RAJKOT (INDIA)
MOID UDDIN AHMAD Assistant Professor, Jaipuria Institute of Management, NOIDA
Dr. SUNEEL ARORA Assistant Professor, G D Goenka World Institute, Lancaster University, NEW DELHI
Mr. P. PRABHU Assistant Professor, Alagappa University, KARAIKUDI
Mr. MANISH KUMAR Assistant Professor, DBIT, Deptt. Of MBA, DEHRADUN
Mrs. BABITA VERMA Assistant Professor, Bhilai Institute Of Technology, DURG
Ms. MONIKA BHATNAGAR Assistant Professor, Technocrat Institute of Technology, BHOPAL
Ms. SUPRIYA RAHEJA Assistant Professor, CSE Department of ITM University, GURGAON
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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195
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2011
Various Techniques in Intrusion
Detection: A Survey
Jay Kant Pratap Singh
Department of Computer Science and Engineering
K.P.Engineering College, Agra, India-282005
Abhishek Kumar Sahu
Department of Computer Science and Engineering
K.P.Engineering College, Agra, India-282005
Title
Author(s)
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
Abstract:
With the rapid expansion of internet computer systems are facing an enormous threat from
external world. There are numerous approaches described in the literature during the recent year
to maintain the information intact. Therefore intrusion detection is becoming important
technology that identifies various network intrusions such as anomalous network behaviour,
unauthorized network access and malicious attacker to computer system. In this paper we
describe a various well known Intrusion detection techniques.
INTRODUCTION:
Any activity or set of activities that attempt to compromise the integrity, confidentiality or
availability of a resource is known as Intrusion. The security of a computer system is one of the
major challenges for researcher. Intrusion prevention alone is not sufficient to protect the
computer from emerging threats. Traditional Intrusion prevention technique such authentication
by using password and information protection by using encryption have been applied to protect
computer systems but there are always some mechanism to exploitable weakness in the systems
due to design and programming errors in the system software due to programming errors. So the
intrusion detection along with prevention is also equally important for computer security. IDS
may perform may be one of misuse detection, anomaly detection or Network intrusion detection
system. Misuse detection relies on matching known patterns of hostile activity against databases
of past attacks. They are highly effective at identifying known attack and vulnerabilities, but
rather poor in identifying new security threats. Anomaly detection will search for something rare
or unusual by applying statistical models or by applying artificial intelligence methods such as
neural network to compare current activity against historical knowledge. Common problems with
anomaly-based systems are that, they often require extensive training data for artificial learning
algorithms, frequent updates. Network-based IDSs collect audit data from the network traffic.
Network-based IDSs offer several advantages. First, network-based IDSs can take advantage of
the standard structure of network protocols, such as TCP/IP. This is a good way to avoid the
confusion resulting from heterogeneity in a distributed system. Second, network-based IDSs
usually run on a separate (dedicated) computer; thus, they do not consume the resources of the
computers that are being protected and most importantly it fits most the real time applications.
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
Any of these IDs scheme is not a silver bullet. So some of the researchers had moved to hybrid
intrusion detection schemes.
REVIEW OF VARIOUS INTRUSION DETECTION METHODS:
A. Rule based IDS.
Rule-Based analysis relies on sets of predefined rules that are provided by an administrator,
automatically created by the system, or both. Expert systems are the most common form of rule-
based intrusion detection approaches. An expert system consists of a set of rules that encode the
knowledge of a human "expert"[1]. These rules are used by the system to make conclusions
about the security-related data from the intrusion detection system. Expert systems permit the
incorporation of an extensive amount of human experience into a computer application that then
utilizes that knowledge to identify activities that match the defined characteristics of misuse and
attack. Unfortunately, expert systems require frequent updates to remain current[2]. Major
problem:- (a) Rule-based systems suffer from an inability to detect attacks scenarios that may
occur over an extended period of time. (b) Intrusion scenarios in which multiple attackers
operate in concert are also difficult for these methods to detect because they do not focus on the
state transitions in an attack, but instead this concentrate on the occurrence of individual
elements. (c) Rule-based systems also lack flexibility in the rule-to-audit record representation.
Slight variations in an attack sequence can affect the activity-rule comparison to a degree that the
intrusion is not detected by the intrusion detection mechanism.
B. Fuzzy Logic Based IDS
Rule based system used restrict the natural ability of attribute to occurs in more than one cluster.
For this reason and with the aid of fuzzy logic, fuzzy clustering can be employed to overcome
the weakness. The membership of a pattern in a given cluster can be any be any value between 0
and 1[3]. In this model a data object belongs to the cluster where it has the highest membership
value. The final output is probabilistic in nature. Step in Fuzzy based IDS are as follows (i) Data
collection: - choosing the best data elements to monitor in the network stream is critical to the
effectiveness of the intrusion detection system. Since Fuzzy is intended to on the network packet
header data rather than the contents of network packets. Fuzzy concentrates on the three main
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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internet protocols: TCP, UDP, and ICMP. Data reduction is critical when monitoring network
data over a lengthy period. (ii) Data Analysis and Profile Generation: - Once enough data is
collected over a two-week collection period, fuzzy inputs sets from each metric are produced [4].
In general, the extents and midpoints of the membership functions were determined with a fuzzy
C-means algorithm. Though, as we shall see, some metrics produce sparse variation that may
require more simple statistical models to define the sets. There are five membership functions in
each input set: LOW, MED-LOW, MEDIUM, MED-HIGH, and HIGH.
Figure: Fuzzy Membership function
(iii) Fuzzy Rules: - With the fuzzy input sets defined, the next step is to write the rules for
detecting each type of attack. A collection of fuzzy rules with the same input and output
variables is called a fuzzy system. We assume that the security administrator can use their expert
knowledge to help create a set of rules for each attack the rules are created using the fuzzy
system editor contained in the Matlab Fuzzy Toolbox.
Neural Network Based IDS:
Neural Networks (NNs) have attracted more attention compared to other techniques. That is
mainly due to the strong discrimination and generalization abilities of Neural Networks that
utilized for classification purposes. An increasing amount of research in the last few years has
investigated the application of Neural Networks to intrusion detection. [5]Neural Networks were
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
specifically proposed to learn the typical characteristics of system’s users and identify
statistically significant variations from their established behavior. In order to apply this approach
to Intrusion Detection, we would have to introduce data representing attacks and non attacks to
the Neural Network to adjust automatically coefficients of this Network during the training
phase. In other words, it will be necessary to collect data representing normal and abnormal
behaviour and train the Neural Network on those data. After training is accomplished, a certain
number of performance tests with real network traffic and attacks should be conducted. Instead
of processing program instruction sequentially, Neural Network based models on simultaneously
explorer several hypotheses make the use of several computational interconnected elements
(neurons); this parallel processing may imply time savings in malicious traffic analysis. The
system is composed of eight modules, as shown in figure
Figure: Neural Network based Ids[6]
(i) Network packet capture device: - It captures data packages from the network and decodes
information. (ii) Pre-processing module (a):- The main aim of this module is to pre-process data
based on extracted characteristics of normal data and also takes care of protocol analysis. First it
converts binary audit data obtained from network into ASCII format. (iii) Normal data detection
module: - To meet the demand of high-speed network, one class classifier is used to identify
normal data. It uses back propagation algorithm and finally, the abnormal data separated are
transferred to the misuse detection module. (iv) Misuse detection module:- It detects known
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
attacks. First, it makes alarm when an attack is found. Then the attack packet information was
detected and abnormal data are together transferred to pre-processing module (b). (v) Statistical
module: - It takes care of data feature statistics based on destination hosts and services. Statistics
based on destination hosts include: the number of connection same as the connected destination
hosts at a time. Statistics based on services include: the number of connection same as the
connected services at a time (vi) Pre-processing module (b):- In this module, data from misuse
detection module and statistical module are implemented integrated processing, and finished
connection record items. Then connection records containing attack information are transferred
to training database of neural network, and the rest of connection records are sent to abnormal
data detection module. (vii) Abnormal data detection module. It uses the same back propagation
algorithm as the normal data detection module. The mainly difference between them are input
data and training data. The former extracts only network connection feature and takes normal
data as training data, but the latter processes feature statistics based on destination hosts and
services, except for network connection feature and content feature, and attack data as its training
data. (viii) Alert response module. A response and alert mechanism is implemented to give an
alarm for aggressive intrusion activities detected and takes measures according to the response
regulations which the users define. Pros and Cons of NNIDS is (a) High tolerance of noisy data
as well as their ability to predict and classify patterns on which they have not been trained. (b)
They are well-suited for continuous-valued inputs and outputs, unlike most decision tree
algorithms (c) Neural network algorithms are inherently parallel; parallelization techniques can
be used to speed up the computation process. (d) Because the output of a neural network is
expressed in the form of a probability the neural network provides a predictive capability to the
detection of instances of misuse (e) It is supervised learning contrary to GA. (f) other
disadvantage of applying neural networks to intrusion detection is the "Black box" nature of the
neural network (Black Box nature means -Acquired knowledge in the form of a network of units
connected by weighted links is difficult for humans to interpret). (g) What could be the initial
topology of neural network? (h) Accuracy of neural network depends on the choosing the initial
weight. So what could be the right choice of initial weight to avoid local minima condition?
C. Genetic Algorithm Based IDS
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
It possibly over come almost all the problem that occurs in Neural network based IDS i.e. It is
Unsupervised in nature and does not have problems mentioned in above (b) and (c) of Neural
network based IDS[7]. The genetic algorithm repeatedly modifies a population of individual
solutions. At each step, the genetic algorithm selects individuals at random from the current
population to be parents and uses them produce the children for the next generation. Over
successive generations, the population "evolves" toward an optimal solution. Three main
operator used in genetic algorithm[8]
Three main operators used in Genetic Algorithm are as follow
(1) Selection rules. Select the individuals, called parents, which contribute to the population at
the next generation.
(2) Crossover rules. Combine two parents to form children for the next generation.
(3) Mutation rules. Apply random changes to individual parents to form children.
Steps of Genetic Algorithm:-
Step1):- Generate Initial individual population
Step 2):- Check the fitness of individual by using the Fitness function.
The individual which has better fitness has high probability to become into parents.
Xi is an individual having detection value f(Xi):
f(Xi)=True Positive Rate (TPR)−False Positive Rate(FPR)
Where TPR = the number of intrusion event which be detected / the number of total intrusion
event.
FPR = the number of normal event which be detected / the number of total normal event.
To calculate the fitness of any individual Xi, We use the Fitness function F(Xi) as.
F(Xi) = f(Xi) - ∑i=1 F(Xi)/n +2
Selection of Fittest rules:- Selection Operator always ensures the best individual must be chosen
i.e. the individual that has the highest selection probability. Selection Probability of Xi can be
calculated as.
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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P(Xi) = F(Xi) / ∑i=n F(Xi)
Step3) Crossover: - Crossover Operator randomly chooses a pairs individuals among those
previously elected to breed and exchange substrings between them. The exchange occurs around
randomly selected crossing points[9].
Let’s take an example of pair of individuals which the length is 16:
Parents 1: 1101111000110001;
Parents 2: 0011101011011111.
The position of crossover point is: 3; 8; 12.
After crossover operates the new individuals are:
Offspring 1: 1101101000111111;
Offspring 2: 0011111011010001.
Step 4: Mutation operator takes one string from the population and randomly alters some value
within it.
Lets an an example of mutation of individual which has the length 16 as follow
Parent: 1111000011010110
Suppose algorithm randomly selects 6th bit to mutate then become.
Offspring: 1111010011010110
The above algorithm can be summarized more appropriately in terms of Flow diagram for better
understanding as follow
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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2011
Figure: Genetic algorithm based Ids
CONCLUSION:
Rule based expert system forms rule using hard computing. Therefore slight variation in the
attack cannot be detected by rule based. Moreover it require frequent updates. On the other hand
Fuzzy logic generate rule that cover more vulnerability than rule formed by expert system using
hard computing. Thirdly genetic algorithm is used to tune the rule and generate the necessary
rules. Genetic algorithm is an unsupervised learning contrary to neural network approach. It
eradicates the problem of topology of network that is inherent to neural network. In neural
network we assume any network topology and initial weight also then perform operation using
back propagation algorithm and check the output by some threshold value. If output is below the
threshold then we again change network topology or weight or both network topology and
weight. But in this approaches there is no question of assuming any network topology and weight
and gives globally optimal solution.
ACKNOWLEDGMENT:
I will thank my teacher assistant professor Uday Pratap Rao, NIT Surat here for fervent help
when I have some troubles in paper writing. I will also thank my class mates in laboratory for
their concern and support both in study and life.
IJRSS Volume 1, Issue 2 ISSN: 2249-2496 _________________________________________________________
A Quarterly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.
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