Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees

Authors:
Cheng Xiang;Png Chin Yong;Lim Swee Meng
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

With increasing connectivity between computers, the need to keep networks secure progressively becomes more vital. Intrusion detection systems (IDS) have become an essential component of computer security to supplement existing defenses. This paper proposes a multiple-level hybrid classifier, a novel intrusion detection system, which combines the supervised tree classifiers and unsupervised Bayesian clustering to detect intrusions. Performance of this new approach is measured using the KDDCUP99 dataset and is shown to have high detection and low false alarm rates.