Research on Intrusion Detection System Based on PCA
Xu Zhang · Computers & Security · 2013
Network intrusion data are often indicated by high-dimension and the inseparability of linear.RBF neural network does not drop the dimension of the function,which directly detects the original data and moves very slowly,and leads to the inefficiency of the real-time network intrusion detection.To reduce the dimension by traditional selective deletion will result in the loss of information and degrade the detection accuracy of network intrusion.In order to improve the network intrusion detection rate and detection speed,we are putting forward the network intrusion detection method(PCA-RBF) which combines a principal component analysis(PCA) and the RBF neural network.PCA-RBF builds the RBF neural network intrusion detection model on the base of PCA network intrusion’s eliminating the redundant information and processing original data dimension.The simulation results show that compared to traditional RBF method,the PCA-RBF reduces the undetected rate,the false detection rate,shortens detection time and improves the detection accuracy rate,which demonstrates its good detection performance.