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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| Format: | Book Section |
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Springer Berlin / Heidelberg
2007
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| _version_ | 1796854120831778816 |
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| author | Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam |
| author_facet | Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam |
| author_sort | Zainal, Anazida |
| collection | ePrints |
| 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.
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| first_indexed | 2024-03-05T18:09:39Z |
| format | Book Section |
| id | utm.eprints-6716 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:09:39Z |
| publishDate | 2007 |
| publisher | Springer Berlin / Heidelberg |
| record_format | dspace |
| spelling | utm.eprints-67162017-07-25T02:41:15Z http://eprints.utm.my/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 |
| spellingShingle | QA75 Electronic computers. Computer science Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam Feature selection using rough-dpso in anomaly intrusion detection |
| title | Feature selection using rough-dpso in anomaly intrusion detection
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| title_full | Feature selection using rough-dpso in anomaly intrusion detection
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| title_fullStr | Feature selection using rough-dpso in anomaly intrusion detection
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| title_full_unstemmed | Feature selection using rough-dpso in anomaly intrusion detection
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| title_short | Feature selection using rough-dpso in anomaly intrusion detection
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| title_sort | feature selection using rough dpso in anomaly intrusion detection |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT zainalanazida featureselectionusingroughdpsoinanomalyintrusiondetection AT maarofmohdaizaini featureselectionusingroughdpsoinanomalyintrusiondetection AT shamsuddinsitimariyam featureselectionusingroughdpsoinanomalyintrusiondetection |