Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities

Abstract

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 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. 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.

References in corpus (9)

• Deep Learning in Neural Networks: An Overview
• Deep Learning for Anomaly Detection: A Survey
• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
• Efficient GAN-Based Anomaly Detection
• Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
• Anomaly Detection using Autoencoders in High Performance Computing Systems
• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
• Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
• EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

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