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Operation efficiency in cyber physical system (CPS) has been significantly improved by digitalization of industrial control systems (ICS). However, digitalization exposes ICS to cyber attacks. Of particular concern are cyber attacks that trigger ICS failure. To determine how cyber attacks can trigger failures and thereby improve the resiliency posture of CPS, this study presents the Resiliency Graph (RG) framework that integrates Attack Graphs (AG) and Fault Trees (FT). RG uses AI planning to establish associations between vulnerabilities and system failures thereby enabling operators to evaluate and manage system resiliency. Our deterministic approach represents both system failures and cyber attacks as a structured set of prerequisites and outcomes using a novel AI planning language. AI planning is then used to chain together the causes and the consequences. Empirical evaluations on various ICS network configurations validate the framework’s effectiveness in capturing how cyber attacks trigger failures and the framework’s scalability. 

The adoption of digital technology in industrial control systems (ICS) enables improved control over operation, ease of system diagnostics and reduction in cost of maintenance of cyber physical systems (CPS). However, digital systems expose CPS to cyber-attacks. The problem is grave since these cyber-attacks can lead to cascading failures affecting safety in CPS. Unfortunately, the relationship between safety events and cyber-attacks in ICS is ill-understood and how cyber-attacks can lead to cascading failures affecting safety. Consequently, CPS operators are ill-prepared to handle cyber-attacks on their systems. In this work, we envision adopting Explainable AI to assist CPS oper-ators in analyzing how a cyber-attack can trigger safety events in CPS and then interactively determining potential approaches to mitigate those threats. We outline the design of a formal framework, which is based on the notion of transition systems, and the associated toolsets for this purpose. The transition system is represented as an AI Planning problem and adopts the causal formalism of human reasoning to asssit CPS operators in their analyses. We discuss some of the research challenges that need to be addressed to bring this vision to fruition.