Context information about a cyber-physical system is collected from various sensors, including non-safety sensors measuring device temperature, motor rotation speed, or instantaneous power consumption of machines. Such contextual information along with operational and business intent of the system under consideration are then used to check whether the current situation is indeed an emergency situation or a normal situation.
Unlike the conventional safety systems that only rely on raw sensor data and safety protocol status packets from safety sensors, which might be spoofed and/or modified, decision on the safety situation in our method is intelligently made by comparing aggregated sensor information from the cyber-physical system and its environment for compliance with pre-configured operational intents that define the normal safe and secure operation of the system. We also show how to integrate Machine Learning (ML) and Artificial Intelligence (AI) into the proposed method for efficient and automated analysis of both intents and aggregated context information to make more intelligent decisions in execution of functional safety protocols.
Full abstract in IEEEXplore DOI: 10.1109/TETC.2023.3251031
Published in: IEEE Transactions on Emerging Topics in Computing, March 6, 2023
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