Diagnosis driven Anomaly Detection for CPS
This addresses the need for holistic solutions in CPS by combining anomaly detection and diagnosis, though it appears incremental as it builds on existing methods like CBD.
The paper tackles the problem of integrating anomaly detection and diagnosis in Cyber-Physical Systems (CPS) by proposing a deep learning-based anomaly detection method to generate inputs for Consistency-Based Diagnosis (CBD), demonstrating strong performance on simulated and real-world datasets.
In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detection and diagnosis must be developed together to provide a holistic solution for diagnosis in CPS. We therefore propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models.