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

• URL: http://arxiv.org/abs/2404.09871v4
• Date: Sun, 28 Jul 2024 13:01:17 GMT
• Title: Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series
• Authors: Daniele Meli,
• Abstract summary: State-of-the-art approaches based on deep learning via neural networks achieve outstanding performance at anomaly recognition. We show that our method has higher training efficiency, outperforms the accuracy of state-of-the-art neural architectures.
• Score: 1.223779595809275
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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