Abstract
In Cyber-Physical Systems (CPS) research, the detection of anomalies—identifying abnormal behaviors—and the diagnosis—pinpointing the underlying root causes—are frequently considered separate, isolated tasks. However, diagnostic algorithms necessitate symptoms, i.e., temporally and spatially isolated anomalies, as inputs. Therefore, integrating anomaly detection and diagnosis is essential for developing a comprehensive diagnostic solution for CPS. This paper introduces a method leveraging deep learning for anomaly detection to effectively identify and localize symptoms within CPS. Our approach is validated on both simulated and real-world CPS datasets, demonstrating robust performance in symptom detection and localization when compared to other state-of-the-art models.
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