Design of Vulnerability and Attack Detection System of Industrial Control System Based on Improved Genetic Algorithm

In this paper, the improved genetic algorithm is used to summarize various information of industrial automatic control system. By exploring the attack detection design direction, the execution conditions of industrial control system are studied, and the vulnerabilities of the system are detected. The maximum and minimum value of the attribute value are calculated and estimated by using the stage search of the attribute of the industrial control system, so as to find the threshold value belonging to the detection design. And the attribute value group is controlled, so that the attack detection points can be counted efficiently and accurately, and the vulnerabilities of the industrial control system can be optimized and upgraded. It was found that the maximum vulnerability passing rate of the industrial control system reached 0.05, the minimum vulnerability triggering anomaly was 55 times, and the detection design quantity of the industrial control system was increased by 92.53% and 185.03%, respectively. Industrial control system design incorporating improved genetic algorithms can improve the efficiency and quality of industrial processes, reduce operational risks and provide accurate data and reporting.