关键词: 特征选择, 分类器, 网络入侵, 参数优化, 核函数参数

Abstract: In order to solve mismatch problem of the feature selection and classifier parameters in network intrusion, this paper proposes a network intrusion detection model （F-SVM） based on coupling feature selection with classifier optimization. The evaluated standard of features is mapped into high-dimensional space by radial basis kernel function to calculate, and the relation between the network feature evaluation and network intrusion classifier is established, so the feature selection stage has solved the parameter design of the classifier, the network intrusion detection model is established and the performance is tested using KDD 99 data. The results show that F-SVM can eliminate unnecessary, redundant features, dimension of network characteristics is significantly reduced, and the optimal parameters of network intrusion classifier are obtained, which improves the network intrusion detection accuracy and detection efficiency.

Key words: features selection, classifier, network intrusion, parameter optimization, kernel function parameter