Intrusion Detection Based on Feature Optimization and SVPSO for Industrial Control System
摘要
With the deep integration of Industrial Control System(ICS) and Internet technologies,it is important to detect system intrusion effectively for secure ICS.As network data of industrial control systems is high-dimensional and nonlinear,this paper applies Fisher score and kernel Principal Component Analysis(PCA) in preprocessing of network data.The standard Particle Swarm Optimization(PSO) algorithm tend to fall into local optimization in optimization of Support Vector Machine(SVM) parameters.To address the problem,a PSO algorithm based on Self-adaptive Mutation(SVPSO),is proposed to build a detection model for system intrusions.Simulation results on the standard dataset show that the detection model comstructed by SVPSO algorithm outperforms BPANN,KNN,random tree and naive Bayes algorithms in terms of detection performance,with the detection accuracy reaching 98.75% while the false alarm rate reduced to 1.22%.