文章基本信息

标题：Clustering-based Feature Selection for Internet Attack Defense
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作者：Jungtaek Seo ; Jungtae Kim ; Jongsub Moon 等
期刊名称：International Journal of Future Generation Communication and Networking
印刷版ISSN：2233-7857
出版年度：2008
卷号：1
期号：1
出版社：SERSC
摘要：Feature selection is as important for intrusion detection as it is for many other problems. A feature selection algorithm can help system administrators to identify and detect new network attacks eﬃciently since appropriately chosen features can improve accuracy of intrusion detection signiﬁcantly as well as can decrease computational overheads of intrusion detection systems. This paper describes a new proposed feature selection algorithms in detecting intrusions using network audit trails. The proposed method is based on our deﬁnition of cluster distance to select good features, and advantages of the proposed feature selection method include independence of data formats (e.g., continuous data or discrete data), suitability for binary classiﬁcation, and improved intrusion detection accuracy. Experimental results using KDDCup99 datasets show that the proposed model can improve intrusion detection accuracy, compared to other algorithms
关键词：Feature selection; intrusion detection; network security