Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patrec.2008.01.008
Title: Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees
Authors: Xiang, C. 
Yong, P.C.
Meng, L.S.
Keywords: Bayesian clustering
Decision tree
False-negative
False-positive
Intrusion detection system (IDS)
Issue Date: 1-May-2008
Citation: Xiang, C., Yong, P.C., Meng, L.S. (2008-05-01). Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees. Pattern Recognition Letters 29 (7) : 918-924. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patrec.2008.01.008
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. © 2008 Elsevier B.V. All rights reserved.
Source Title: Pattern Recognition Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/50893
ISSN: 01678655
DOI: 10.1016/j.patrec.2008.01.008
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