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Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques

by S. Revathi, A. Malathi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 6
Year of Publication: 2013
Authors: S. Revathi, A. Malathi
10.5120/13116-0458

S. Revathi, A. Malathi . Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques. International Journal of Computer Applications. 75, 6 ( August 2013), 22-27. DOI=10.5120/13116-0458

@article{ 10.5120/13116-0458,
author = { S. Revathi, A. Malathi },
title = { Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 6 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number6/13116-0458/ },
doi = { 10.5120/13116-0458 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:33.603967+05:30
%A S. Revathi
%A A. Malathi
%T Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 6
%P 22-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to access of malicious data in internet, Intrusion detection system becomes an important element in system security that controls real time data and leads to huge dimensional problem, so a data pre-processing is necessary to reduce haziness and to clean network data. To reduce false positive rate and to increase efficiency of detection, the paper proposed a new swarm intelligence technique to solve complex optimization problem. The paper work based on hybrid Simplified Swarm Optimization (SSO) algorithm to pre-process the data. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organizing ability to emerge in highly distributed control problem area, and is versatile, strong and cost effective to resolve complex computing environments. It recognize not only known attacks but also filters noisy and irrelevant data that may result on knowledge Discovery and Data Mining (KDDCup 1999) dataset and compared to a new hybrid Partial Swarm Optimization with Random Forest (PSO-RF) and with other benchmark classifiers. The testing result shows that the proposed method provides competitively high detection rates and produce a near optimal solution.

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Index Terms

Computer Science
Information Sciences

Keywords

Swarm intelligence Simplified Swarm Optimization Partial Swarm Optimization Random Forest Intrusion detection

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