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Reseach Article

Use of Genetic Algorithm in Network Security

by L. M. R. J. Lobo, Suhas B. Chavan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 8
Year of Publication: 2012
Authors: L. M. R. J. Lobo, Suhas B. Chavan
10.5120/8438-2221

L. M. R. J. Lobo, Suhas B. Chavan . Use of Genetic Algorithm in Network Security. International Journal of Computer Applications. 53, 8 ( September 2012), 1-7. DOI=10.5120/8438-2221

@article{ 10.5120/8438-2221,
author = { L. M. R. J. Lobo, Suhas B. Chavan },
title = { Use of Genetic Algorithm in Network Security },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 8 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number8/8438-2221/ },
doi = { 10.5120/8438-2221 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:53:33.858175+05:30
%A L. M. R. J. Lobo
%A Suhas B. Chavan
%T Use of Genetic Algorithm in Network Security
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 8
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

After overcoming some drawbacks from an improved genetic feedback algorithm based network security policy framework. A motivation was experienced for the need of a strong network security policy framework. In this paper a strong network model for security function is presented. A gene for a network packet is defined through fitness function and a method to calculate fitness function is explained. The basic attacks encountered can be categorized as buffer overflow, array index out of bound, etc. A stress is given on passive attack, active attack, its types and brute force attack. An analysis on recent attacks and security is provided. Finally the best policy using a comparator is found. The main aim of this paper is threat detection, time optimization, performance increase in terms of accuracy and policy automation. For experimenting real data set from the internet and local network is used. Jpcap, winpcap, Colasoft Capsa 7 open source software are used for implementation. 73% efficiency is achieved because we add 3 networking terms such as payload, checksum, and sequence number. A client server environment is made use of.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic algorithm Network security policy framework Fitness function