文献类型：期刊文献

英文题名：A rough set and SVM based intrusion detection classifier

作者：Gu, Chunhua[1]; Zhang, Xueqin[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2009

卷号：2

起止页码：106

外文期刊名：2nd International Workshop on Computer Science and Engineering, WCSE 2009

收录：EI(收录号：20101412821486)

语种：英文

外文关键词：Vectors - Digital storage - Rough set theory - Quadratic programming - Intrusion detection

摘要：Support vector machine-based intrusion detection methods are increasingly being researched because it can detect novel attacks. But solving a support vector machine problem is a typical quadratic optimization problem, which is influenced by the feature dimensions and number of training samples. Feature selection or attribution reduction can help reduce the SVM classification time and saving memory space effectively. This paper concerns using rough set for attribution ranking and reducing and using support vector machine for intrusion detection classification. An elicitation attribution reduction algorithm (EARA) based on attribution significance and discernibility matrix is presented and three data discretization algorithms were applied to identify the important attributions. The classification performance of the presented algorithm and classical SVM were compared in accuracy, time, false positive rate, and detection rate. The experiment results show the presented algorithm has ability to reduce the complexity of the structure of the support vector machine, simplify training sets and decrease training time and data storage without obviously sacrificing the detection correctness. ? 2009 IEEE.