Anomaly-Based Network Intrusion Detection Using Outlier Subspace Analysis: A Case Study

被引：0

作者：

Kershaw, David ^{[1
]}

Gao, Qigang ^{[1
]}

Wang, Hai ^{[2
]}

机构：

[1] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 3J5, Canada

[2] St Marys Univ, Sobey Sch Business, San Antonio, TX 78228 USA

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE | 2011年 / 6657卷

关键词：

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：

This paper employs SPOT (Stream Projected Outlier deTector) as a prototype system for anomaly-based intrusion detection and evaluates its performance against other major methods. SPOT is capable of processing high-dimensional data streams and detecting novel attacks which exhibit abnormal behavior, making it a good candidate for network intrusion detection. This paper demonstrates SPOT is effective to distinguish between normal and abnormal processes in a UNIX System Call dataset.

引用

页码：234 / 239

页数：6

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