Operational Impact-Driven Cybersecurity Risk Assessment for Industrial Cyber-Physical Systems

Industrial Cyber-Physical Systems (CPS) face escalating cybersecurity threats capable of causing significant operational disruptions, yet existing risk assessment methods often fail to adequately evaluate the threats posed by sophisticated multi-stage attacks. This paper introduces a novel methodology to identify the most critical cyberattack scenarios targeting industrial CPS operations. Our approach integrates an artificial intelligence (AI) based sequential decision-making optimization engine with a simulation model of the industrial process and a dynamical system based on cyber-states that represent the cyberattack as it evolves according to the adversary's actions. This allows for the discovery of attack scenarios that maximize the disruption of operational key performance indicators of the industrial CPS. A key contribution of this work is the systematic integration of well-accepted cybersecurity data sources and control frameworks to define a principled “cost” for each adversary action. This principled cost, combined with a configurable attacker “budget” representing total adversary effort, enables the AI engine to identify the most critical attack paths and quantify the maximum operational disruption achievable. The resulting information is invaluable for operators, particularly in domains like power systems, offering crucial guidance to achieve an enhanced cybersecurity posture. Our methodology's efficacy is demonstrated through testing and validation on both a synthetic power system scenario, based on a modified IEEE 123-bus network, and a real-world application to the distribution grid operated by Holy Cross Energy in Colorado, United States.