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Deep Learning-based Anomaly Detection in Cyber-physical Systems
ACM Computing Surveys ( IF 16.6 ) Pub Date : 2021-05-25 , DOI: 10.1145/3453155
Yuan Luo 1 , Ya Xiao 2 , Long Cheng 3 , Guojun Peng 1 , Danfeng (Daphne) Yao 2
Affiliation  

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, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. To this end, deep learning-based anomaly detection (DLAD) methods have been proposed. In this article, 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. Also, we discuss the limitations and open problems of these methods. Moreover, to give users insights into choosing proper DLAD methods in practice, we experimentally explore the characteristics of typical neural models, the workflow of DLAD methods, and the running performance of DL models. Finally, we discuss the deficiencies of DL approaches, our findings, and possible directions to improve DLAD methods and motivate future research.

中文翻译:

网络物理系统中基于深度学习的异常检测

异常检测对于确保网络物理系统 (CPS) 的安全至关重要。然而,由于 CPS 的日益复杂和更复杂的攻击,面对不断增长的数据量并需要特定领域知识的传统异常检测方法无法直接应用于应对这些挑战。为此,已经提出了基于深度学习的异常检测(DLAD)方法。在本文中,我们回顾了 CPS 中最先进的 DLAD 方法。我们根据异常的类型、策略、实施和评估指标提出了一种分类法,以了解当前方法的基本属性。此外,我们利用这种分类法来识别和突出每个 CPS 域中的新特征和设计。此外,我们讨论了这些方法的局限性和未解决的问题。而且,为了让用户在实践中选择合适的 DLAD 方法,我们通过实验探索典型神经模型的特征、DLAD 方法的工作流程以及 DL 模型的运行性能。最后,我们讨论了 DL 方法的缺陷、我们的发现以及改进 DLAD 方法和激励未来研究的可能方向。
更新日期:2021-05-25
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