关键词: 网络入侵, 特征选择, 支持向量机, 遗传算法

Abstract: To improve network intrusion detection rate, this paper proposes a network intrusion detection algorithm based on joint optimization algorithm for feature selection and classifier. The algorithm uses the relationship between the feature selection and classifier parameters. The feature and classifier parameters are taken as genetic algorithm individuals while the network intrusion detection rate as the evaluation function of genetic algorithm, then the optimization parameters are obtained by selection, crossover and mutation of genetic algorithm, and the joint optimization algorithm is tested by KDD 1999 data. The experimental results show that the proposed algorithm has overcome the degression of detection ability resulting from unmatched of the features and the classifier parameters, and has improved the network intrusion detection rate compared with other algorithms; it provides a new way for network intrusion detection.

Key words: network intrusion, feature selection, Support Vector Machine（SVM）, Genetic Algorithm（GA）