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...
| Main Authors: | , , |
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| Format: | Article |
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Springer-Verlag Berlin Heildelberg
2007
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| Online Access: | http://eprints.utm.my/5599/ |
| _version_ | 1848891091417300992 |
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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 |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:52:27Z |
| format | Article |
| id | utm-5599 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T20:52:27Z |
| publishDate | 2007 |
| publisher | Springer-Verlag Berlin Heildelberg |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-55992017-09-12T08:35:32Z http://eprints.utm.my/5599/ Feature selection using Rough-DPSO in anomaly intrusion detection Zainal, Anazida Maarof, Mohd Aizaini Shamsuddin, Siti Mariyam QA76 Computer software 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-Verlag Berlin Heildelberg 2007-08-29 Article PeerReviewed Zainal, Anazida and Maarof, Mohd Aizaini and Shamsuddin, Siti Mariyam (2007) Feature selection using Rough-DPSO in anomaly intrusion detection. ICCSA 2007, Lecture Notes in Computer Science Part 1 , 4705 . pp. 512-524. ISSN 0302-9743 https://link.springer.com/chapter/10.1007/978-3-540-74472-6_42 |
| spellingShingle | QA76 Computer software 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 |
| 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_short | Feature selection using Rough-DPSO in anomaly intrusion detection |
| title_sort | feature selection using rough-dpso in anomaly intrusion detection |
| topic | QA76 Computer software |
| url | http://eprints.utm.my/5599/ http://eprints.utm.my/5599/ |