Integrated Network Intrusion Detection Model in Simulation
Tao Liu · Jisuanji fangzhen · 2011
Study the problem of network security.Network intrusions are of diversity and complexity,and have redundant information,the traditional neural network intrusion detection methods have the disadvantages of complicated network structure,long training time and low accuracy.In order to improve the network intrusion detection rate and the network security,the principal component analysis and RBF neural network are combined and formed an integrated network intrusion detection model.The network intrusion data are pretreated by principal component analysis to reduce the characteristic dimension,eliminate redundant information and reduce the RBF neural network input.Then pretreatment features are used as neural network's inputs,network intrusion types are used as neural network's outputs,and the RBF neural network intrusion detection model is established.Finally,network data were detected by using the network intrusion detection model.In Matlab,the integrated model is tested by the DARPA network intrusion datasets.Simulation results show that the integration model accuracy is higher than the traditional network intrusion detection method,the mistake examining rate is reduced,and the network intrusion detection speed is speeded up,It is a real-time detection tool for the network intrusion detection.