计算机应用与软件2011，Vol.28Issue(12)：60-63,155,5.
基于属性约简和SVM参数优化的入侵检测方法
AN ATTRIBUTE REDUCTION AND SVM PARAMETER OPTIMIZATION BASED INTRUSION DETECTION METHOD
摘要
Abstract
SVM is strongly applicable to small sampled, nonlinear or high dimensional classification issues. But there are alsodisadvantages with SVM such as long training period, too much storage occupation by sample sets and so on. The thesis proposes an attribute reduction and parameter optimization based SVM intrusion detection method, which uses the rough set theory to execute feature reduction on sample sets and uses the improved network search algorithm to optimize SVM parameters, so that removes those properties that don't have impact on intrusion detection. Hence the disadvantages like long training period and large storage requirement are overcome. Experiment using KDD99 data set shows that the new method is an effective intrusion detection method. It not only accelerates the training speed, but also improves the accuracy of intrusion detection.关键词
支持向量机/粗糙集/属性约简/参数优化/入侵检测Key words
SVM (support vector machine)/Rough set/Attribute reduction/Parameter optimization/Intrusion detection分类
信息技术与安全科学引用本文复制引用
周志德,李汉彪..基于属性约简和SVM参数优化的入侵检测方法[J].计算机应用与软件,2011,28(12):60-63,155,5.基金项目
国家自然科学基金(60975027). （60975027）
