融合灰狼优化算法在工控系统入侵检测中的应用

Aiming at the characteristics of large data volume and high dimensions in the current industrial control system, a grey wolf optimization integrated random black hole (RBHGWO) algorithm incorporating a random black hole (RBH) strategy is proposed. When the wolf group updates the position of the next generation grey wolf, the proposed algorithm simulates the attraction of black holes, so that the individual in the wolf group can move faster towards the current global optimal solution, and enhances the convergence speed of the proposed algorithm. Meanwhile, individuals are randomly attracted by black holes, which maintain the local search ability of the proposed algorithm. Compared with particle swarm optimization (PSO), random black hole particle swarm optimization (RBHPSO), GWO algorithm and survival of fitness grey wolf optimization (SFGWO) algorithm using test functions, the experimental results show that the RBHGWO algorithm has fast convergence speed and excellent convergence accuracy. Moreover, based on the data set of Tennessee-Eastman (TE) simulation platform, the situation of industrial control systems is simulated by attacking from the covert intrusion. The experimental results show that the RBHGWO algorithm has obvious advantages in convergence accuracy, iteration speed and stability in the feature selection of intrusion detection of industrial control systems.