Intrusion Detection Method Based on Improved Neural Network

Abstract

In order to improve the ability of network intrusion detection and blind separation, an improved network intrusion detection algorithm is proposed based on improved neural network. The network intrusion information transmission channel model is constructed, the feature extraction and signal separation of network intrusion are carried out by adaptive weighted control method, and the correlation parameters of network intrusion are estimated by combining time-frequency joint estimation method. The node location and intrusion intensity of network intrusion are calculated accurately, and intrusion detection is carried out according to the result of parameter estimation. BP neural network is used to classify and identify network intrusion, the accuracy of intrusion detection and the ability of blind source location are improved. The simulation results show that the accuracy of network intrusion detection is higher, and the location accuracy of intrusion information source is higher, and the network security performance is improved.