Network Intrusion Detection by using Particle Swarm Optimization and Neural Network

In order to improve the effectiveness of network intrusion detection, a network intrusion detection model based on particle swarm optimization algorithm and neural network is proposed. Firstly, the feature of network intrusion detection is collected, and important features are selected by using particle swarm optimization algorithm to effectively remove the invalid feature; then BP neural network is used to build intrusion detection classifier, finally KDD 99 data is used to analyze performance of the model. The results show that the proposed model can improve accuracy of network intrusion detection, and the detection speed can meet the practical requirements of network security.