Intrusion detection based on data mining

Authors:
Jian Yin;Fang Mei;Gang Zhang
Affiliations:
Department of Computer Science, Sun Yat-Sen University, Guangzhou, China;Department of Computer Science, Sun Yat-Sen University, Guangzhou, China;Department of Computer Science, Sun Yat-Sen University, Guangzhou, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

Many traditional algorithms use single metric generated by multi-events to detect intrusion by comparison with a certain threshold. In this paper we present a metric vector-based algorithm to detect intrusion while introducing the sample distance for both discrete and continuous data in order to improve the algorithm on heterogeneous dataset. Experiments on MIT lab Data show that the proposed algorithm is effective and efficient.