Feature selection using rough set in intrusion detection
Most of existing Intrusion Detection Systems use all data features to detect an intrusion. Very little works address the importance of having a small feature subset in designing an efficient intrusion detection system. Some features are redundant and some contribute little to the intrusion detection...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2006
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/3228/1/TENCON_2006.pdf |
| _version_ | 1796853535904628736 |
|---|---|
| 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 existing Intrusion Detection Systems use all data features to detect an intrusion. Very little works address the importance of having a small feature subset in designing an efficient intrusion detection system. Some features are redundant and some contribute little to the intrusion detection process. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in building an intrusion detection system. Rough Set was also used to classify the data. Here, we used KDD Cup 99 data. Empirical results indicate that Rough Set is comparable to other feature selection techniques deployed by few other researchers. |
| first_indexed | 2024-03-05T18:00:58Z |
| format | Conference or Workshop Item |
| id | utm.eprints-3228 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:00:58Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | utm.eprints-32282017-08-29T06:29:52Z http://eprints.utm.my/3228/ Feature selection using rough set in intrusion detection Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam QA75 Electronic computers. Computer science Most of existing Intrusion Detection Systems use all data features to detect an intrusion. Very little works address the importance of having a small feature subset in designing an efficient intrusion detection system. Some features are redundant and some contribute little to the intrusion detection process. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in building an intrusion detection system. Rough Set was also used to classify the data. Here, we used KDD Cup 99 data. Empirical results indicate that Rough Set is comparable to other feature selection techniques deployed by few other researchers. 2006-11-14 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/3228/1/TENCON_2006.pdf Zainal, Anazida and Maarof, Mohd. Aizaini and Shamsuddin, Siti Mariyam (2006) Feature selection using rough set in intrusion detection. In: IEEE TENCON 2006, 14-17th November 2006, Hongkong. |
| spellingShingle | QA75 Electronic computers. Computer science Zainal, Anazida Maarof, Mohd. Aizaini Shamsuddin, Siti Mariyam Feature selection using rough set in intrusion detection |
| title | Feature selection using rough set in intrusion detection |
| title_full | Feature selection using rough set in intrusion detection |
| title_fullStr | Feature selection using rough set in intrusion detection |
| title_full_unstemmed | Feature selection using rough set in intrusion detection |
| title_short | Feature selection using rough set in intrusion detection |
| title_sort | feature selection using rough set in intrusion detection |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/3228/1/TENCON_2006.pdf |
| work_keys_str_mv | AT zainalanazida featureselectionusingroughsetinintrusiondetection AT maarofmohdaizaini featureselectionusingroughsetinintrusiondetection AT shamsuddinsitimariyam featureselectionusingroughsetinintrusiondetection |