Anomaly-based intrusion detection using fuzzy rough clustering
It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, w...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2006
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| Online Access: | http://eprints.utm.my/7458/1/Abdullah_Abd_Hanan_2006_Anomaly_BAsed_Intrusion_Detection_Fuzzy.pdf http://eprints.utm.my/7458/ http://dx.doi.org/10.1109/ICHIT.2006.253508 |
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| _version_ | 1845471814720946176 |
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| author | Chimphlee, Witcha Abdullah, Abdul Hanan Sap, M. N. M Srinoy, Surat Chimphlee, Siriporn |
| author_facet | Chimphlee, Witcha Abdullah, Abdul Hanan Sap, M. N. M Srinoy, Surat Chimphlee, Siriporn |
| author_sort | Chimphlee, Witcha |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods |
| format | Conference or Workshop Item |
| id | my.utm.eprints-7458 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2006 |
| record_format | eprints |
| spelling | my.utm.eprints-74582017-08-30T01:34:55Z http://eprints.utm.my/7458/ Anomaly-based intrusion detection using fuzzy rough clustering Chimphlee, Witcha Abdullah, Abdul Hanan Sap, M. N. M Srinoy, Surat Chimphlee, Siriporn QA75 Electronic computers. Computer science It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods 2006 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/7458/1/Abdullah_Abd_Hanan_2006_Anomaly_BAsed_Intrusion_Detection_Fuzzy.pdf Chimphlee, Witcha and Abdullah, Abdul Hanan and Sap, M. N. M and Srinoy, Surat and Chimphlee, Siriporn (2006) Anomaly-based intrusion detection using fuzzy rough clustering. In: Proceedings - 2006 International Conference on Hybrid Information Technology, ICHIT 2006 , 9th-11th Nov 2006. http://dx.doi.org/10.1109/ICHIT.2006.253508 |
| spellingShingle | QA75 Electronic computers. Computer science Chimphlee, Witcha Abdullah, Abdul Hanan Sap, M. N. M Srinoy, Surat Chimphlee, Siriporn Anomaly-based intrusion detection using fuzzy rough clustering |
| title | Anomaly-based intrusion detection using fuzzy rough clustering |
| title_full | Anomaly-based intrusion detection using fuzzy rough clustering |
| title_fullStr | Anomaly-based intrusion detection using fuzzy rough clustering |
| title_full_unstemmed | Anomaly-based intrusion detection using fuzzy rough clustering |
| title_short | Anomaly-based intrusion detection using fuzzy rough clustering |
| title_sort | anomaly-based intrusion detection using fuzzy rough clustering |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/7458/1/Abdullah_Abd_Hanan_2006_Anomaly_BAsed_Intrusion_Detection_Fuzzy.pdf http://eprints.utm.my/7458/ http://dx.doi.org/10.1109/ICHIT.2006.253508 |
| url_provider | http://eprints.utm.my/ |