Hybrid Knowledge and Data Driven Synthesis of Runtime Monitors for Cyber-Physical Systems.

Recent advances in sensing and computing technology have led to the proliferation of Cyber-Physical Systems (CPS) in safety-critical domains. However, the increasing device complexity, shrinking technology sizes, and shorter time to market have resulted in significant challenges in ensuring the reliability, safety, and security of CPS. This article presents a hybrid knowledge and data-driven approach for designing run-time context-aware safety monitors that can detect early signs of hazards and mitigate them in CPS. We propose a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with two optimization approaches for scenario-specific refinement and integration of STL specifications using data collected from closed-loop CPS simulations. We demonstrate the effectiveness of our approach in simulation using an autonomous driving system (ADS) and two closed-loop artificial pancreas systems (APS) as well as a publicly-available clinical trial dataset. The results show that a safety monitor developed with the proposed approaches demonstrates up to 4.7 times increase in average prediction accuracy (F1 score) over several well-designed baseline monitors while reducing both false-positive and false-negative rates in most scenarios.