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Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization

by Sunita Patel, Jyoti Sondhi, Anand Motvani, Anurag Shrivastava
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 18
Year of Publication: 2014
Authors: Sunita Patel, Jyoti Sondhi, Anand Motvani, Anurag Shrivastava
10.5120/17628-8409

Sunita Patel, Jyoti Sondhi, Anand Motvani, Anurag Shrivastava . Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization. International Journal of Computer Applications. 100, 18 ( August 2014), 34-37. DOI=10.5120/17628-8409

@article{ 10.5120/17628-8409,
author = { Sunita Patel, Jyoti Sondhi, Anand Motvani, Anurag Shrivastava },
title = { Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 18 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number18/17628-8409/ },
doi = { 10.5120/17628-8409 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:29.558712+05:30
%A Sunita Patel
%A Jyoti Sondhi
%A Anand Motvani
%A Anurag Shrivastava
%T Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 18
%P 34-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Reduces the file size and increase the performances of classification and intrusion detection technique used in current research trend. The reduction of file size and number of attribute used dimension reduction algorithm and optimization algorithm. Various authors used genetic algorithm, ANT colony optimization and neural network. In this paper used particle of swarm optimization technique for feature reduction and feature selection for support vector machine classification process. The proposed algorithm implemented in MATLAB software and used DARPA dataset for evaluation of proposed method. Our empirical result shows that better detection ratio in compression of other exiting technique such as FCMNN, GSVM.

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Index Terms

Computer Science
Information Sciences

Keywords

IDS Feature Reduction SVM POS

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