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Simulation-basedFaultInjection(FI)ishighlyrecommended by functional safety standards in the automotive and aerospace domains, in order to “support the argumentation of completeness and correctness of a system architectural design with respect to faults” (ISO 26262). We argue that a library of failure models facilitates this process. Such a library, firstly, supports completeness claims through, e.g., an extensive and systematic collection process. Secondly, we argue why failure model specifications should be executable—to be implemented as FI operators within a simulation framework—and parametrizable—to be relevant and accurate for different systems. Given the distributed nature of automo- tive and aerospace development processes, we moreover argue that a data-flow-based definition allows failure models to be applied to black- box components. Yet, existing sources for failure models provide frag- mented, ambiguous, incomplete, and redundant information, often meet- ing neither of the above requirements. We therefore introduce a library of 18 executable and parameterizable failure models collected with a sys- tematic literature survey focusing on automotive and aerospace Cyber- Physical Systems (CPS). To demonstrate the applicability to simulation- based FI, we implement and apply a selection of failure models to a real- world automotive CPS within a state-of-the-art simulation environment, and highlight their impact. 

Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them in MCPS. We present a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with an optimization method for patient-specific refinement of STL formulas based on real or simulated faulty data from the closed-loop system for the generation of monitor logic. We evaluate our approach in simulation using two state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results show the context-aware monitor achieves up to 1.4 times increase in average hazard prediction accuracy (F1score) over several baseline monitors, reduces false-positive and false-negative rates, and enables hazard mitigation with a 54% success rate while decreasing the average risk for patients. 

Medical Cyber-physical Systems (MCPS) are vul- nerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them in MCPS. We present a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with an optimization method for patient-specific refinement of STL formulas based on real or simulated faulty data from the closed-loop system for the gener- ation of monitor logic. We evaluate our approach in simulation using two state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results show the context-aware monitor achieves up to 1.4 times increase in average hazard prediction accuracy (F1- score) over several baseline monitors, reduces false-positive and false-negative rates, and enables hazard mitigation with a 54% success rate while decreasing the average risk for patients.