Risk Analysis for Railway Signaling Safety Data Network Based on Extend Bayesian Attack Graph

Abstract

The railway signaling safety data network is a key network to ensure the safety of train operation and improve transportation efficiency. Its information security is closely related to the safe and efficient operation of trains. This paper proposes an extend Bayesian attack graph model for risk analysis of railway signaling safety data network. Based on the traditional Bayesian attack graph, the model introduces the protection node, and describes the causality among network attack, protection and network state through the Bayesian network. First of all, based on the analysis of the vulnerabilities and possible malicious attack paths in the railway signaling safety data network, the extend Bayesian attack graph model is established with the system functional safety accidents as the target nodes. Then calculate the probability of functional safety accidents by Bayesian network, and combine the impact of different accidents to evaluate the risk of system information security. In addition, according to different attacks and the implementation of different protective measures, evidence node state can be set up, and the extend Bayesian attack graph model is able to use Bayesian inference to analyze the system risk.