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Received:May 19, 2011 Revised:June 29, 2011
Received:May 19, 2011 Revised:June 29, 2011
中文摘要: 入侵检测系统面临的主要问题是计算量大,特征选择被引入解决这一问题。针对现有方法的缺点,利用改进的粒子群算法来搜索最优特征子集,提出了一种基于混合Catfish PSO和最小二乘支持向量机的特征选择方法,利用混合的Catfish BPSO和Catfish PSO选择特征子集并同步对LSSVM的参数进行优化,最后建立了一个基于该特征选择方法的入侵检测模型。在KDD Cup 99数据集上进行的实验结果表明该模型的检测性能较高。
Abstract:The main issue of Intrusion detection systems is large computation, feature selection was introduced to solve the problem. According to the shortcomings of existing methods, this paper uses improved Particle Swarm Optimization to search optimal feature subset, proposes a feature selection method based on hybrid CatfishPSO and Least Square Support Vector Machine, uses combined CatfishBPSO and CatfishPSO to select feature subset and optimize the parameters of LSSVM simultaneously, and build a Intrusion detection model based on the feature selection method above. Experiments on KDD Cup 99 show that the model has a good detection performance.
keywords: feature selection particle swarm optimization least square support vector machine intrusion detection
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王卫平,唐志煦.基于混合CatfishPSO-LSSVM 特征选择的入侵检测.计算机系统应用,2012,21(1):85-89
WANG Wei-Ping,TANG Zhi-Xu.Intrusion Detection Based on Hybrid CatfishPSO-LSSVM Feature Selection.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):85-89
王卫平,唐志煦.基于混合CatfishPSO-LSSVM 特征选择的入侵检测.计算机系统应用,2012,21(1):85-89
WANG Wei-Ping,TANG Zhi-Xu.Intrusion Detection Based on Hybrid CatfishPSO-LSSVM Feature Selection.COMPUTER SYSTEMS APPLICATIONS,2012,21(1):85-89
