Intrusion detection based on k-means clustering and OneR classification
Intrusion detection system (IDS) is used to detect various kinds of attacks in interconnected network. Many machine learning methods have also been introduced by researcher recently to obtain high accuracy and detection rate. Unfortunately, a potential drawback of all those methods is the rate of fa...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68939/ http://psasir.upm.edu.my/id/eprint/68939/1/Intrusion%20detection%20based%20on%20k-means%20clustering%20and%20OneR%20classification.pdf |
| _version_ | 1848856268822806528 |
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| author | Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura |
| author_facet | Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura |
| author_sort | Muda, Zaiton |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Intrusion detection system (IDS) is used to detect various kinds of attacks in interconnected network. Many machine learning methods have also been introduced by researcher recently to obtain high accuracy and detection rate. Unfortunately, a potential drawback of all those methods is the rate of false alarm. However, our proposed approach shows better results, by combining clustering (to identify groups of similarly behaved samples, i.e. malicious and non-malicious activity) and classification techniques (to classify all data into correct class categories). The approach, KM+1R, combines the k-means clustering with the OneR classification technique. The KDD Cup '99 set is used as a simulation dataset. The result shows that our proposed approach achieve a better accuracy and detection rate, particularly in reducing the false alarm. |
| first_indexed | 2025-11-15T11:38:58Z |
| format | Conference or Workshop Item |
| id | upm-68939 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:38:58Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-689392019-06-12T02:06:48Z http://psasir.upm.edu.my/id/eprint/68939/ Intrusion detection based on k-means clustering and OneR classification Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura Intrusion detection system (IDS) is used to detect various kinds of attacks in interconnected network. Many machine learning methods have also been introduced by researcher recently to obtain high accuracy and detection rate. Unfortunately, a potential drawback of all those methods is the rate of false alarm. However, our proposed approach shows better results, by combining clustering (to identify groups of similarly behaved samples, i.e. malicious and non-malicious activity) and classification techniques (to classify all data into correct class categories). The approach, KM+1R, combines the k-means clustering with the OneR classification technique. The KDD Cup '99 set is used as a simulation dataset. The result shows that our proposed approach achieve a better accuracy and detection rate, particularly in reducing the false alarm. IEEE 2011 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68939/1/Intrusion%20detection%20based%20on%20k-means%20clustering%20and%20OneR%20classification.pdf Muda, Zaiton and Mohamed Yassin, Warusia and Sulaiman, Md. Nasir and Udzir, Nur Izura (2011) Intrusion detection based on k-means clustering and OneR classification. In: 7th International Conference on Information Assurance and Security (IAS 2011), 5-8 Dec. 2011, Melaka, Malaysia. (pp. 192-197). 10.1109/ISIAS.2011.6122818 |
| spellingShingle | Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura Intrusion detection based on k-means clustering and OneR classification |
| title | Intrusion detection based on k-means clustering and OneR classification |
| title_full | Intrusion detection based on k-means clustering and OneR classification |
| title_fullStr | Intrusion detection based on k-means clustering and OneR classification |
| title_full_unstemmed | Intrusion detection based on k-means clustering and OneR classification |
| title_short | Intrusion detection based on k-means clustering and OneR classification |
| title_sort | intrusion detection based on k-means clustering and oner classification |
| url | http://psasir.upm.edu.my/id/eprint/68939/ http://psasir.upm.edu.my/id/eprint/68939/ http://psasir.upm.edu.my/id/eprint/68939/1/Intrusion%20detection%20based%20on%20k-means%20clustering%20and%20OneR%20classification.pdf |