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

作　　者：罗尚平[1] 刘才铭[1] LUO Shangping;LIU Caiming(School of Computer Science,Leshan Normal University,Leshan 614004,China)

机构地区：[1]乐山师范学院计算机科学学院,四川乐山614004

出　　处：《现代电子技术》2017年第10期31-34,共4页Modern Electronics Technique

基　　金：国家自然科学基金青年项目(61103249)

摘　　要：针对当前的神经网络检测算法在强干扰下的网络入侵检测准确拦截性不好的问题,提出一种基于粒子群算法和支持向量机的网络入侵检测方法。构建网络入侵的特征信号模型,采用二阶自适应格型IIR陷波器进行入侵信息的抗干扰处理;粒子群算法进行自适应寻优提取网络入侵特征的最优解,SVM进行入侵信息分类,实现网络入侵有效检测;并进行仿真测试。结果表明,采用该方法进行网络入侵检测的准确拦截概率较高,误检和漏检概率较低,保障了网络安全。As the accuracy of the current neural network detection algorithm to detect and intercept network intrusion instrong interference is not high enough,a detection method based on particle swarm optimization algorithm and support vectormachine 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 resultsshow 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 

分 类 号：TN711-34[电子电信—电路与系统] TP393[自动化与计算机技术—计算机应用技术]