文献类型：会议论文

英文题名：Network Intrusion Detection Method Based on Radial Basic Function Neural Network

作者：Tian, Jingwen[1];Gao, Meijuan[1,2];Zhang, Fan[2]

通讯作者：Tian, J.

机构：[1]Beijing Union Univ, Coll Automat, Beijing, Peoples R China;[2]Beijing Univ Chem Technol, Sch Informat Sci, 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]College of Automation, Beijing Union University, Beijing, 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：1st International Conference on E-Business and Information System Security

会议日期：MAY 23-24, 2009

会议地点：Wuhan, PEOPLES R CHINA

语种：英文

外文关键词：intrusion detection; intrusion behaviors; radial basic function neural network; K-nearest neighbor algorithm

摘要：Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of radial basic function neural network (RBFNN), an intrusion detection method based on radial basic function neural network is presented in this paper. We construct the structure of RBFNN that used for detection network intrusion behavior, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong function approach and fast convergence of radial basic function neural network, the network intrusion detection method based on radial basic function neural network 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.