Optimal defence strategy for chemical industry park cyber–physical systems: An improved logit dynamic evolutionary game‐theoretic method

Abstract With the development of Industry 4.0, cyber–physical systems have been widely applied in chemical industry parks to promote intelligent production. However, the cyber–physical systems in chemical industry parks are vulnerable to emerging cascading risks such as cyber attacks, which may lead to severe accidents. Effective security defence strategies are particularly important to ensure the stable operation of the systems. Therefore, this study proposes an optimal defence strategy generation method for the chemical industry park cyber–physical systems based on logit dynamics and evolutionary game theory. Firstly, this method constructs an attack–defence game model based on the improved logit dynamics and evolutionary game theory. By introducing the rationality degree (RD) and the prevention‐control level (PCL) factor into the original logit dynamic evolution equation, it is used to explain and analyze the dynamic choice process and mechanism of strategic choices. Secondly, a novel attack–defence utility quantification method based on the characteristics of vulnerabilities is proposed, which innovatively combines the confidence level of attack operations and the impact of vulnerabilities to quantify the utility. Finally, a real experimental platform is built for case study, and four scenarios are established to conduct numerical analysis on the game evolution model. Furthermore, the influence of the RD and the PCL factor on the evolution process of attack–defence strategies is analysed. The experimental results verify the accuracy and effectiveness of the proposed method in generating optimal defence strategies.