Interpretable Deep Learning Architectures for Decision-Critical Cyber-Physical Systems

The increasing reliance on deep learning (DL) models for decision-critical tasks, such as anomaly detection in Cyber-Physical Systems (CPS), presents a significant challenge due to their inherent "black-box" nature, compromising trust and hindering root cause analysis during safety-critical events. This paper proposes an Interpretable Deep Recurrent Network (IDRN) architecture, integrating a specialized attention mechanism and post-hoc SHAP (SHapley Additive exPlanations) analysis, specifically tailored for real-time time-series data from smart grid CPS. The IDRN is designed to achieve high anomaly detection performance while providing intrinsic and extrinsic model transparency. We evaluate the architecture on the SWaT (Secure Water Treatment) dataset, demonstrating that the IDRN maintains state-of-the-art accuracy ($F_1$-score $>0.94$) while simultaneously generating low-latency feature attribution maps. These maps enable system operators to pinpoint the exact sensor and actuator readings responsible for the predicted anomaly, significantly enhancing safety, facilitating faster response times, and improving model trustworthiness in decision-critical environments.