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Intrusion detection based on K-means clustering and Naïve Bayes classification

Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection is often associated with high false alarm with moderate accuracy and detection...

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Main Authors:	Muda, Zaiton, Mohamed Yassin, Warusia, Sulaiman, Md. Nasir, Udzir, Nur Izura
格式:	Conference or Workshop Item
語言:	English
出版:	IEEE 2011
在線閱讀:	http://psasir.upm.edu.my/id/eprint/68866/1/Intrusion%20detection%20based%20on%20K-means%20clustering%20and%20Na%C3%AFve%20Bayes%20classification.pdf http://psasir.upm.edu.my/id/eprint/68866/
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id	my.upm.eprints.68866
record_format	eprints
spelling	my.upm.eprints.688662019-06-11T01:41:47Z http://psasir.upm.edu.my/id/eprint/68866/ Intrusion detection based on K-means clustering and Naïve Bayes classification Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection is often associated with high false alarm with moderate accuracy and detection rates when it's unable to detect all types of attacks correctly. To overcome this problem, we propose an hybrid learning approach through combination of K-Means clustering and Naïve Bayes classification. The proposed approach will be cluster all data into the corresponding group before applying a classifier for classification purpose. An experiment is carried out to evaluate the performance of the proposed approach using KDD Cup'99 dataset. Result show that the proposed approach performed better in term of accuracy, detection rate with reasonable false alarm rate. IEEE 2011 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68866/1/Intrusion%20detection%20based%20on%20K-means%20clustering%20and%20Na%C3%AFve%20Bayes%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 Naïve Bayes classification. In: 7th International Conference on Information Technology in Asia (CITA 2011), 12-13 July 2011, Kuching, Sarawak. . 10.1109/CITA.2011.5999520
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
language	English
description	Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection is often associated with high false alarm with moderate accuracy and detection rates when it's unable to detect all types of attacks correctly. To overcome this problem, we propose an hybrid learning approach through combination of K-Means clustering and Naïve Bayes classification. The proposed approach will be cluster all data into the corresponding group before applying a classifier for classification purpose. An experiment is carried out to evaluate the performance of the proposed approach using KDD Cup'99 dataset. Result show that the proposed approach performed better in term of accuracy, detection rate with reasonable false alarm rate.
format	Conference or Workshop Item
author	Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura
spellingShingle	Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura Intrusion detection based on K-means clustering and Naïve Bayes classification
author_facet	Muda, Zaiton Mohamed Yassin, Warusia Sulaiman, Md. Nasir Udzir, Nur Izura
author_sort	Muda, Zaiton
title	Intrusion detection based on K-means clustering and Naïve Bayes classification
title_short	Intrusion detection based on K-means clustering and Naïve Bayes classification
title_full	Intrusion detection based on K-means clustering and Naïve Bayes classification
title_fullStr	Intrusion detection based on K-means clustering and Naïve Bayes classification
title_full_unstemmed	Intrusion detection based on K-means clustering and Naïve Bayes classification
title_sort	intrusion detection based on k-means clustering and naïve bayes classification
publisher	IEEE
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/68866/1/Intrusion%20detection%20based%20on%20K-means%20clustering%20and%20Na%C3%AFve%20Bayes%20classification.pdf http://psasir.upm.edu.my/id/eprint/68866/
_version_	1643839329864253440
score	13.251813

Intrusion detection based on K-means clustering and Naïve Bayes classification

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