Intrusion Detection of Industrial Control System Based on Improved Bat Algorithm

Aiming at the local minima problem of the standard bat algorithm (BA),this paper makes two improvements.Firstly,the current local optimal solution distribution is considered during the updating of bats' positions.Secondly,the random variation operation in differential evolution (DE) algorithm is introduced into BA to increase the diversity of the population and enhance the local search ability of the BA algorithm.Besides,the superiority of the proposed algorithm is illustrated by means of typical test functions.Moreover,the proposed algorithm is applied to the parameters optimization of support vector machine (SVM) classifier in industrial control system (ICS) intrusion detection model.The simulation results from the standard dataset for industrial system intrusion detection show that,compared with DE,particle swarm optimization (PSO) and genetic algorithm (GA),the optimized SVM intrusion detection model via the proposed algorithm can effectively improve the detection rate,false negative rate,and false alarm rate.