Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series

• Published in: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) pp. 4120 - 4125
• Main Author: Meli, Daniele
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28-08-2024
• Subjects: Anomaly detection; Benchmark testing; Cyber-physical systems; Real-time systems; Robot sensing systems; Safety; Service robots; Time series analysis; Training; Training data
• ISSN: 2161-8089
• DOI: 10.1109/CASE59546.2024.10711445

Online unsupervised detection of anomalies is crucial to guarantee the correct operation of cyber-physical systems and the safety of humans interacting with them. State-of-the-art approaches based on deep learning via neural networks achieve outstanding performance at anomaly recognition, evaluating the discrepancy between a normal model of the system (with no anomalies) and the real-time stream of sensor time series. However, large training data and time are typically required, and explainability is still a challenge to identify the root of the anomaly and implement predictive maintainance. In this paper, we use causal discovery to learn a normal causal graph of the system, and we evaluate the persistency of causal links during real-time acquisition of sensor data to promptly detect anomalies. On two benchmark anomaly detection datasets, we show that our method has higher training efficiency, outperforms the accuracy of state-of-the-art neural architectures and correctly identifies the sources of > 10 different anomalies. The code is at https://github.com/Isla-lab/causal_anomaly_detection.