现代电子技术2017，Vol.40Issue(10)：31-34,4.DOI:10.16652/j.issn.1004-373x.2017.10.009
粒子群算法和SVM的网络入侵检测
A detection method based on particle swarm optimization algorithm and SVM dealing with network intrusion
摘要
Abstract
As the accuracy of the current neural network detection algorithm to detect and intercept network intrusion in strong interference is not high enough,a detection method based on particle swarm optimization algorithm and support vector machine to deal with the network intrusion is put forward,and the feature signal model of network intrusion is built. The two-or-der adaptive lattice IIR notch filter is adopted for anti-jamming processing of intrusion information. The particle swarm optimiza-tion algorithm is used to extract the optimal solution of network intrusion features in adaptive optimizing mode. SVM is em-ployed for intrusion information classification to realize the effective detection of network intrusion. The simulation test results show that the method has high accurate intercepting probability and low false dismissal detection probability for network intru-sion detection. It can guarantee the network security.关键词
粒子群算法/支持向量机/网络入侵/检测算法Key words
particle swarm optimization/support vector machine/network intrusion/detection algorithm分类
信息技术与安全科学引用本文复制引用
罗尚平,刘才铭..粒子群算法和SVM的网络入侵检测[J].现代电子技术,2017,40(10):31-34,4.基金项目
国家自然科学基金青年项目(61103249) （61103249）
