Network Intrusion Detection Based on Relevance Vector Machine Optimized by Particle Swarm Optimization Algorithm

It is significant to protect the information of network and avoid it under attack by constructing network intrusion detection system. In order to overcome the drawbacks of support vector machine, network intrusion detection based on relevance vector machine optimized by particle swarm optimization algorithm (PSO-RVM) is presented in the paper. Relevance vector machine is a sparse probability model based on support vector machine. Relevance vector machine has not only higher detection accuracy, but also better real-time than support vector machine. In the study, particle swarm optimization algorithm is used to determine nuclear parameter of relevance vector machine. Finally, the case data are used to testify and analyze the performance of the proposed model. The experimental results show that PSO-RVM has greater prediction accuracy than PSO-SVM, BP neural network.