Industrial Control Network Security Situation Assessment Based on SAE-RBF

Abstract

With the in-depth development of “two integration”, industrial control network security situation is more and more serious, industrial control network security protection work is more and more important. Aiming at the characteristics of sparse and complex dimensions of industrial control network security data, combining with the conceptual model of network security situation awareness, this paper proposes a network security situation assessment method, which uses stack self-encoder to process sparse data and uses radial basis neural network to fit complex nonlinear characteristics. It effectively extracts the data features, realizes the analysis and understanding of the data, and completes the security situation assessment, which provides a new method for the security situation assessment of the industrial control network.