文献类型：会议论文

英文题名：Intrusion Detection Method Based on Classify Support Vector Machine

作者：Gao, Meijuan[1];Tian, Jingwen[1];Xia, Mingping[1]

第一作者：高美娟

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Coll Automat, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Automat, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：2nd International Conference on Intelligent Computation Technology and Automation

会议日期：OCT 10-11, 2009

会议地点：Changsha, PEOPLES R CHINA

语种：英文

外文关键词：intrusion detection; support vector machine; genetic algorithm; intrusion behaviors

摘要：Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of support vector machine (SVM), an intrusion detection method based on classify SVM is presented in this paper. The SVM network structure for intrusion detection is established, and use the genetic algorithm (GA) to optimize SVM parameters, thereby enhancing the convergence rate and the detection accuracy. We discussed and analyzed the affect factors of network intrusion behaviors. With the ability of strong self-learning and well generalization of SVM, the intrusion detection method based on classify SVM can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.