文献类型：会议论文

英文题名：Network Intrusion Detection Method Based on Improved Simulated Annealing Neural Network

作者：Gao, Meijuan[1];Tian, Jingwen[1]

第一作者：高美娟

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Dept Automat Control, 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, Dept Automat Control, 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：International Conference on Measuring Technology and Mechatronics Automation

会议日期：APR 11-12, 2009

会议地点：Zhangjiajie, PEOPLES R CHINA

语种：英文

外文关键词：intrusion behaviors; intrusion detection; simulated annealing algorithm; neural network

摘要：Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of neural network, an intrusion detection method based on improved simulated annealing neural network (ISANN) is presented in this paper. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is organic combined with Powell algorithm to form improved simulated annealing mixed optimize algorithm, instead of gradient falling algorithm of BP network to train network weight. It can get higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence of ISANN, the network intrusion detection method 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.