期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2016
卷号：10
期号：4
页码：241-248
DOI：10.14257/ijsia.2016.10.4.23
出版社：SERSC
摘要：According to the relationship between feature subset and parameters of RBF neural network, in order to improve the intrusion detection accuracy, it proposed an improved particle swarm optimization neural network of network intrusion detection model. Network feature subset and parameters of RBF neural network were regarded as a particle, through collaboration and information exchange between particles to find the optimal feature subset and parameters of RBF neural network, so as to establish the optimal network intrusion detection model, and using KDD Cup 99 data sets to carry out simulation experiment. The simulation results showed that, IPSO-RBF neural network reduced the feature dimensions, and the better parameters of RBF neural network was obtained then, which is a kind of network intrusion detection model with high detection accuracy and high speed.
