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Feature selection using rough-dpso in anomaly intrusion detection

Most of the existing IDS use all the features in network packet to evaluate and look for known intrusive patterns. Some of these features are irrelevant and redundant. The drawback to this approach is a lengthy detection process. In real-time environment this may degrade the performance of an IDS. T...

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Main Authors: Zainal, Anazida, Maarof, Mohd. Aizaini, Shamsuddin, Siti Mariyam
Format: Book Section
Published: Springer Berlin / Heidelberg 2007
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/6716/
http://www.springerlink.com/content/f7gg9x253q767337/fulltext.pdf
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spelling my.utm.67162017-07-25T02:41:15Z http://eprints.utm.my/id/eprint/6716/ Feature selection using rough-dpso in anomaly intrusion detection Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam QA75 Electronic computers. Computer science Most of the existing IDS use all the features in network packet to evaluate and look for known intrusive patterns. Some of these features are irrelevant and redundant. The drawback to this approach is a lengthy detection process. In real-time environment this may degrade the performance of an IDS. Thus, feature selection is required to address this issue. In this paper, we use wrapper approach where we integrate Rough Set and Particle Swarm to form a 2-tier structure of feature selection process. Experimental results show that feature subset proposed by Rough-DPSO gives better representation of data and they are robust. Springer Berlin / Heidelberg 2007-08-29 Book Section PeerReviewed Zainal, Anazida and Maarof, Mohd. Aizaini and Shamsuddin, Siti Mariyam (2007) Feature selection using rough-dpso in anomaly intrusion detection. In: Computational science and its applications – ICCSA 2007. Springer Berlin / Heidelberg, pp. 512-524. ISBN 978-3-540-74468-9 http://www.springerlink.com/content/f7gg9x253q767337/fulltext.pdf 10.1007/978-3-540-74472-6
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zainal, Anazida
Maarof, Mohd. Aizaini
Shamsuddin, Siti Mariyam
Feature selection using rough-dpso in anomaly intrusion detection
description Most of the existing IDS use all the features in network packet to evaluate and look for known intrusive patterns. Some of these features are irrelevant and redundant. The drawback to this approach is a lengthy detection process. In real-time environment this may degrade the performance of an IDS. Thus, feature selection is required to address this issue. In this paper, we use wrapper approach where we integrate Rough Set and Particle Swarm to form a 2-tier structure of feature selection process. Experimental results show that feature subset proposed by Rough-DPSO gives better representation of data and they are robust.
format Book Section
author Zainal, Anazida
Maarof, Mohd. Aizaini
Shamsuddin, Siti Mariyam
author_facet Zainal, Anazida
Maarof, Mohd. Aizaini
Shamsuddin, Siti Mariyam
author_sort Zainal, Anazida
title Feature selection using rough-dpso in anomaly intrusion detection
title_short Feature selection using rough-dpso in anomaly intrusion detection
title_full Feature selection using rough-dpso in anomaly intrusion detection
title_fullStr Feature selection using rough-dpso in anomaly intrusion detection
title_full_unstemmed Feature selection using rough-dpso in anomaly intrusion detection
title_sort feature selection using rough-dpso in anomaly intrusion detection
publisher Springer Berlin / Heidelberg
publishDate 2007
url http://eprints.utm.my/id/eprint/6716/
http://www.springerlink.com/content/f7gg9x253q767337/fulltext.pdf
_version_ 1643644626312101888
score 13.2014675

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