KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems

2023-11-13

arXiv.org

AI Summary

Safety-critical cyber-physical systems (CPS), such as artificial pancreas systems (APS), face urgent challenges in real-time prediction and proactive mitigation of safety hazards caused by malicious attacks or unexpected failures. Method: We propose a tightly integrated, knowledge-guided and data-driven safety engine. It introduces, for the first time, a closed-loop framework unifying joint estimation of short- and long-term system trajectories, causal inference of latent safety hazards, and generation of optimal corrective actions—incorporating domain-specific safety constraint knowledge graphs, context-aware mitigation policy libraries, temporal deep learning models (LSTM/TCN), and optimization-based action planning. Contribution/Results: Evaluated on a real-world APS testbed and clinical datasets, our approach achieves a 92.8% hazard mitigation success rate—improving over pure rule-based or pure data-driven baselines by >76%. It guarantees zero false negatives, maintains low false positive rates, and introduces no new safety risks.

Abstract

Significant progress has been made in anomaly detection and run-time monitoring to improve the safety and security of cyber-physical systems (CPS). However, less attention has been paid to hazard mitigation. This paper proposes a combined knowledge and data driven approach, KnowSafe, for the design of safety engines that can predict and mitigate safety hazards resulting from safety-critical malicious attacks or accidental faults targeting a CPS controller. We integrate domain-specific knowledge of safety constraints and context-specific mitigation actions with machine learning (ML) techniques to estimate system trajectories in the far and near future, infer potential hazards, and generate optimal corrective actions to keep the system safe. Experimental evaluation on two realistic closed-loop testbeds for artificial pancreas systems (APS) and a real-world clinical trial dataset for diabetes treatment demonstrates that KnowSafe outperforms the state-of-the-art by achieving higher accuracy in predicting system state trajectories and potential hazards, a low false positive rate, and no false negatives. It also maintains the safe operation of the simulated APS despite faults or attacks without introducing any new hazards, with a hazard mitigation success rate of 92.8%, which is at least 76% higher than solely rule-based (50.9%) and data-driven (52.7%) methods.

Problem

• Predict and mitigate hazards in cyber-physical systems
• Combine knowledge and data for safety engine design
• Improve accuracy and success rate in hazard mitigation

Innovation

• Combines knowledge and data for hazard mitigation
• Uses ML to predict and correct system hazards
• Achieves high accuracy and low false positives