Practical causal models for cyber-physical systems

Unlike faults in classical systems, faults in Cyber-Physical Systems will often be caused by the system’s interaction with its physical environment and social context, rendering these faults harder to diagnose. To complicate matters further, knowledge about the behavior and failure modes of a system are often collected in different models. We show how three of those models, namely attack trees, fault trees, and timed failure propagation graphs can be converted into Halpern-Pearl causal models, combined into a single holistic causal model, and analyzed with actual causality reasoning to detect and explain unwanted events. Halpern-Pearl models have several advantages over their source models, particularly that they allow for modeling preemption, consider the non-occurrence of events, and can incorporate additional domain knowledge. Furthermore, such holistic models allow for analysis across model boundaries, enabling detection and explanation of events that are beyond a single model. Our contribution here delineates a semi-automatic process to (1) convert different models into Halpern-Pearl causal models, (2) combine these models into a single holistic model, and (3) reason about system failures. We illustrate our approach with the help of an Unmanned Aerial Vehicle case study.