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

被引：88
|
作者：
Xiang, Cheng [1 ]
Yong, Png Chin [1 ]
Meng, Lim Swee [1 ]
机构：
[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
来源：
PATTERN RECOGNITION LETTERS | 2008年 / 29卷 / 07期
关键词：
Bayesian clustering; decision tree; false-negative; false-positive; intrusion detection system (IDS);
D O I：
10.1016/j.patrec.2008.01.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码：918 / 924
页数：7
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