Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities
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Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods cannot be directly applied to thwart such issues, which also need domain-specific knowledge and handle the growing volume of data. Deep learning-based anomaly detection (DLAD) methods have been proposed to achieve unsupervised detection in the era of CPS big data. In this paper, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. We summarise a list of publicly available datasets for training and evaluation. We also discuss our findings, the limitations of existing studies, and possible directions to improve DLAD methods.
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