Intrusion detection using relevance vector machine based on cloud particle swarm optimization
LI Guo-don · Kongzhi yu juece · 2015
Intrusion detection can protect computer network information. In the research based on this method, due to the relevance vector machine(RVM) has high sparseness and uses probability factor in predict, which is superior to the support vector machine(SVM) in the network intrusion detection. However, the kernel function parameters of RVM are estimated by experience. Therefore, a kind of RVM method based on the cloud particle swarm optimization(PSO) algorithm is proposed, which adopts the CPSO algorithm to determine the kernel parameter of RVM, then builds RVM model and uses the one-against-one classification method to finish multi-class intrusion detection. The experimental researches on intrusion detection show that the proposed method is superior to the common RVM-based detection method and has high prediction accuracy in intrusion detection.