文章基本信息

标题：Intrusion Detection Scheme based on IPSO-RBF
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作者：WU, Wen-Tie ; LI, Min ; LIU, Bo 等
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2013
卷号：8
期号：10
页码：2269-2276
DOI：10.4304/jnw.8.10.2269-2276
语种：English
出版社：Academy Publisher
摘要：In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network intrusion detection model of IPSO-RBF, made convergence and disturbance variation analysis on improving optimized particle swarm optimization, finally made a experimental simulation to this detection model, the result showed: compared with traditional models, the neural network intrusion detection of improved particle swarm optimization was faster and has a higher accuracy rate as well as efficiency.
关键词：Neural Network;Particle Swarm Optimization;Detection;Parameter Setting