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iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning by Tian, Jiyu; Li, Mingchu; Chen, Liming; Wang, Zumin is a scholarly article available to read on EtoBox.

What is iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning about?

Anomaly detection for cyber-physical systems (ADCPS) is crucial in identifying faults and potential attacks by analyzing the time series of sensor measurements and actuator states. However, current methods lack adaptation to data distribution shifts in both temporal and spatial dimensions as cyber-physical systems evolve. To tackle this issue, we propose an incremental meta-learning-based approach, namely iADCPS, which can continuously update the model through limited evolving normal samples to reconcile the distribution gap between evolving and historical time series. Specifically, We first introduce a temporal mixup strategy to align data for data-level generalization which is then combined with the one-class meta-learning approach for model-level generalization. Furthermore, we develop a non-parametric dynamic threshold to adaptively adjust the threshold based on the probability density of the abnormal scores without any anomaly supervision. We empirically evaluate the effectiveness of the iADCPS using three publicly available datasets PUMP, SWaT, and WADI. The experimental results demonstrate that our method achieves 99.0%, 93.1%, and 78.7% F1-Score, respectively, which outperf

Author

Tian, Jiyu; Li, Mingchu; Chen, Liming; Wang, Zumin

Published

2025

Language

EN