Design of multiple-level tree classifiers for intrusion detection system

被引：0

作者：

Xiang, C ^{[1
]}

Chong, MY ^{[1
]}

Zhu, HL ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119260, Singapore

来源：

2004 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2004年

关键词：

data minning; multiple-level decision tree; intrusion detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Intrusion detection system (IDS) has recently emerged as an important component for enhancing information system security. To effectively build corresponding rules and patterns of computer attack scenarios and system vulnerabilities, data mining has been widely used in constructing and maintaining IDS. Based on statistical characteristics of specific intrusion types, a novel approach of using multiple-level tree classifiers is proposed in this paper to identify intrusions. Performance of this new algorithm is compared to other popular approaches such as MADAM ID [1].

引用

页码：873 / 878

页数：6

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