Structural Learning Techniques for Bayesian Attack Graphs in Cyber Physical Power Systems

Updating the structure of attack graph templates based on real-time alerts from Intrusion Detection Systems (IDS), in an Industrial Control System (ICS) network, is currently done manually by security experts. But, a highly-connected smart power systems, that can inadvertently expose numerous vulnerabilities to intruders for targeting grid resilience, needs automatic fast updates on learning attack graph structures, instead of manual intervention, to enable fast isolation of compromised network to secure the grid. Hence, in this work, we develop a technique to first construct a prior Bayesian Attack Graph (BAG) based on a predefined threat model and a synthetic communication network for a cyber-physical power system. Further, we evaluate a few score-based and constraint-based structural learning algorithms to update the BAG structure based on real-time alerts, based on scalability, data dependency, time complexity and accuracy criteria.