Hierarchical feature selection in IDS

Generally, IDS use all the features in network packet to evaluate and look for intrusive patterns. This data contains redundant and some give false correlation. Thus, feature selection is required to address this issue. This study integrates a statistical approach called Rough Set and evolutionary c...

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Main Authors: Maarof, Mohd. Aizaini, Zainal, Anazida, Shamsuddin, Siti Mariyam
Format: Conference or Workshop Item
Published: 2007
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Online Access:http://eprints.utm.my/id/eprint/25370/
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Institution: Universiti Teknologi Malaysia
id my.utm.25370
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spelling my.utm.253702017-08-06T07:42:49Z http://eprints.utm.my/id/eprint/25370/ Hierarchical feature selection in IDS Maarof, Mohd. Aizaini Zainal, Anazida Shamsuddin, Siti Mariyam QA75 Electronic computers. Computer science Generally, IDS use all the features in network packet to evaluate and look for intrusive patterns. This data contains redundant and some give false correlation. Thus, feature selection is required to address this issue. This study integrates a statistical approach called Rough Set and evolutionary computing approach called 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. 2007 Conference or Workshop Item PeerReviewed Maarof, Mohd. Aizaini and Zainal, Anazida and Shamsuddin, Siti Mariyam (2007) Hierarchical feature selection in IDS. In: Postgraduate Annual Research Seminar (PARS’ 07), 2007, UTM, Johor Bahru.
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
Maarof, Mohd. Aizaini
Zainal, Anazida
Shamsuddin, Siti Mariyam
Hierarchical feature selection in IDS
description Generally, IDS use all the features in network packet to evaluate and look for intrusive patterns. This data contains redundant and some give false correlation. Thus, feature selection is required to address this issue. This study integrates a statistical approach called Rough Set and evolutionary computing approach called 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 Conference or Workshop Item
author Maarof, Mohd. Aizaini
Zainal, Anazida
Shamsuddin, Siti Mariyam
author_facet Maarof, Mohd. Aizaini
Zainal, Anazida
Shamsuddin, Siti Mariyam
author_sort Maarof, Mohd. Aizaini
title Hierarchical feature selection in IDS
title_short Hierarchical feature selection in IDS
title_full Hierarchical feature selection in IDS
title_fullStr Hierarchical feature selection in IDS
title_full_unstemmed Hierarchical feature selection in IDS
title_sort hierarchical feature selection in ids
publishDate 2007
url http://eprints.utm.my/id/eprint/25370/
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