Quantitative Method for Security Situation of the Power Information Network Based on the Evolutionary Neural Network - Yuan, Quande; Pi, Yuzhen; Kou, Lei; Zhang, Fangfang; Ye, Bo (Frontiers in Energy Research, 2022)
Cyber security is the security cornerstone of the digital transformation of the power grid and the construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network performance, ignoring the impact of various power application services on the security situation, so the quantification results cannot fully reflect the power information network risk state. This paper proposes a method for quantifying security situation of power information network based on evolutionary neural network. Firstly, the security posture system architecture is designed by analyzing the business characteristics of power information network applications. Secondly, combining the importance of power application business, the spatial element index system of coupled interconnection is established from three dimensions of network reliability, threat and vulnerability. Then, the BP neural network optimized by genetic evolutionary algorithm is incorporated into the element index calculation process, and the quantitative model of security posture of power information network based on evolutionary neural network is constructed. Finally, a simulation experiment environment is built according to a power sector network topology, and the effectiveness and robustness of the method proposed in the paper are verified.