A hierarchical model-based security risk assessment approach for the Train Control & Monitoring System

Abstract The Train Control & Monitoring System (TCMS) plays a crucial role in ensuring the reliable and safe operation of rail trains. However, in recent years, the adoption of Ethernet and wireless technologies has exposed TCMS to serious security challenges. Risk assessment, as a critical component of dynamic security protection, plays a vital role in identifying system vulnerabilities and guiding mitigation efforts. Therefore, the accurate identification of system security risks is essential for ensuring the safe and stable operation of TCMS. To address this need, this paper proposes a hierarchical model-based assessment approach. This method utilizes a Bayesian network to model attack behaviours and incorporates cellular automata to model the propagation impacts of attacks. It allows for the inference of probabilities associated with TCMS functions being compromised, thereby quantifying system risks posed by attacks. Then it focuses on the modelling process of the evaluation model and the calculation method for quantifying risk. The effectiveness of the approach is demonstrated through a simulated TCMS scenario involving a high-speed train.