A graph embedding-based approach for automatic cyber-physical power system risk assessment to prevent and mitigate threats at scale

Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large-scale power infrastructures as cyber-physical systems is still in its early stages. Drawing inspiration from machine-learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large-scale cyber-physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high-dimensional structures in low-dimensional spaces, facilitating advanced machine-learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding-based approach, the authors present a structured and methodical framework for risk assessment in cyber-physical systems. The proposed graph embedding-based risk analysis framework aims to provide a more insightful perspective on cyber-physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber-physical power system models: the Western System Coordinating Council (WSCC) 9-Bus System and the Illinois 200-Bus System, thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.