Feature selection using rough set in intrusion detection

被引：0

作者：

Zainal, Anazida ^{[1
]}

Maarof, Mohd Aizaini ^{[1
]}

Shamsuddin, Siti Mariyam ^{[1
]}

机构：

[1] Univ Technol Malaysia, Fac Comp Sci & Informat Syst, Skudai 81310, Malaysia

来源：

TENCON 2006 - 2006 IEEE REGION 10 CONFERENCE, VOLS 1-4 | 2006年

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：

Most of existing Intrusion Detection Systems use all data features to detect an intrusion. Very little works address the importance of having a small feature subset in designing an efficient intrusion detection system. Some features are redundant and some contribute little to the intrusion detection process. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in building an intrusion detection system. Rough Set was also used to classify the data. Here, we used KDD Cup 99 data. Empirical results indicate that Rough Set is comparable to other feature selection techniques deployed by few other researchers.

引用

页码：2026 / +

页数：2

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