A Security Situation Prediction Model for Industrial Control Network Based on EP-CMA-ES

Abstract

To solve the problem of industrial control network (ICN) security situation prediction, this paper proposes a security situation prediction model for ICN based on evidential reasoning (ER) and belief rule base (BRB). First, this paper analyzes multiple factors influencing the security situation of the ICN, establishes a framework for security situation assessment, employs the ER algorithm for attribute fusion, and derives the security situation value of the ICN. Second, using historical data combined with expert knowledge, a security situation prediction model for ICN based on the BRB is constructed. Additionally, an extended projection covariance matrix adaptive evolution strategy (EP-CMA-ES) optimization algorithm is proposed, which is employed to optimize the parameters of the prediction model. The model not only comprehensively uses qualitative knowledge and quantitative data, but also integrates more uncertain information, and the reasoning process is interpretable. It also solves the subjectivity problem of expert knowledge, overcomes the problem of small amount of data caused by the difficulty of collecting industrial control safety data, and improves the accuracy of model prediction. Finally, prediction experiments were conducted on industrial datasets, confirming the feasibility and effectiveness of the security situation prediction model for ICN and the EP-CMA-ES optimization algorithm proposed in this paper.