基于BPSO-SVM的网络入侵特征选择和检测  

Selection and Detection of Network Intrusion Feature Based on BPSO-SVM

作　　者：高海华[1] 杨辉华[1] 王行愚[1] 

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《计算机工程》2006年第8期37-39,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(69974014);国家重点基础研究发展规划项目(2002CB312200);教育部高校博士点基金资助项目(20040251010)

摘　　要：采用改进的二进制粒子群优化进行入侵特征子集选择,粒子群中每个粒子代表一个选择的特征子集,结合支持向量机使用该特征子集所对应的数据集进行分类,正确分类结果作为该粒子的适应度,通过粒子群优化实现最优入侵特征选择。改进的BPSO方法中通过引入粒子群依概率整体变异来避免陷入局部最优,同时采用粒子禁忌搜索列表来扩大粒子搜索范围和避免重复计算;SVM中采用基于粒度的网格搜索来获得最优核参数。最后用KDD 99标准数据集进行实验研究,结果表明该方法能获得满意的检测效果。In the proposed algorithm, every particle in the swarm stands for a selected subset of features, The fitness of particle is defined as the correct classification percentage by SVM using a training set whose patterns are represented using only the selected subset of features. Thus through particle swarm optimization to achieve intrusion feature selection and classification. A probabilistic mutation of BPSO is adopted to avoid local optimal and a tabu search table is used to enlarge particle swarm＇s search space and avoid repeated compntation. The results of experiment demonstrate that applying a hybrid of BPSO-SVM in intrusion detection System can be an effective way for feature selection and detecting intrusions via using the data sets of KDD cup 99.

关 键 词：二进制粒子群优化 支持向量机 异常检测 特征选择 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]