Diagnosis driven Anomaly Detection for CPS
Henrik S. Steude Lukas Moddemann Alexander Diedrich Jonas Ehrhardt Oliver Niggemann
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.