【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。更多还原

【Abstract】 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 computation.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.更多还原