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Reseach Article

Anomaly based Intrusion Detection System using Genetic Algorithm and K-Centroid Clustering

by Biswapriyo Chakrabarty, Omit Chanda, Md. Saiful Islam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 11
Year of Publication: 2017
Authors: Biswapriyo Chakrabarty, Omit Chanda, Md. Saiful Islam
10.5120/ijca2017913762

Biswapriyo Chakrabarty, Omit Chanda, Md. Saiful Islam . Anomaly based Intrusion Detection System using Genetic Algorithm and K-Centroid Clustering. International Journal of Computer Applications. 163, 11 ( Apr 2017), 13-17. DOI=10.5120/ijca2017913762

@article{ 10.5120/ijca2017913762,
author = { Biswapriyo Chakrabarty, Omit Chanda, Md. Saiful Islam },
title = { Anomaly based Intrusion Detection System using Genetic Algorithm and K-Centroid Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 11 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number11/27438-2017913762/ },
doi = { 10.5120/ijca2017913762 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:54.972236+05:30
%A Biswapriyo Chakrabarty
%A Omit Chanda
%A Md. Saiful Islam
%T Anomaly based Intrusion Detection System using Genetic Algorithm and K-Centroid Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 11
%P 13-17
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Internet is being expanded because of the enhancement of today’s networks and with these expansion different types of unauthorized activities building up to make the network vulnerable. Many researchers are working around the world to protect the systems from any kind of unauthorized access. In this study we have implemented an Intrusion Detection System based on K-Centroid Clustering and Genetic Algorithm to achieve a better detection rate and false positive rate. In our system training set is classified into different clusters based on K-Centroid clustering and then GA is performed to check each connection of the test set and finally result has been obtained for every specific connection. We have used both Kdd99Cup and NSLKDD dataset to get the experiment result of our system. Finally analyzing with those data we have got a decent detection rate in our implemented system.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Computer Security Intrusion Detection Intrusion Detection Systems Genetic Algorithm K-centroid Clustering