综述物理信息系统异常检测技术，助你选对方法防故障与攻击
Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques
- 按数据、模型、混合与系统四类梳理异常检测方法
- 指出各类技术优缺点，指导实际安全系统设计
- 适合关注工业安全、智能系统可靠性的研究者
在日益互联的世界中，网络物理系统（CPS）对医疗、交通、制造等关键行业至关重要，融合了物理过程与计算智能。然而，这些系统的安全性面临严峻挑战。传感器故障或网络攻击引发的异常可能导致灾难性后果，因此有效的异常检测对防止损害和业务中断至关重要。本文全面综述了CPS中的异常检测技术，将方法分为数据驱动（机器学习、深度学习、机器学习-深度学习集成）、模型驱动（数学、基于不变量）、混合数据-模型（物理信息神经网络）以及系统导向四类。分析揭示了各类技术的优劣，为构建更安全可靠的系统提供实用指南。通过识别当前研究空白，旨在激发未来工作，提升自动化世界中CPS的安全性与适应性。
原文摘要 · Abstract (English)
In an increasingly interconnected world, Cyber-Physical Systems (CPS) are essential to critical industries like healthcare, transportation, and manufacturing, merging physical processes with computational intelligence. However, the security of these systems is a major concern. Anomalies, whether from sensor malfunctions or cyberattacks, can lead to catastrophic failures, making effective detection vital for preventing harm and service disruptions. This paper provides a comprehensive review of anomaly detection techniques in CPS. We categorize and compare various methods, including data-driven approaches (machine learning, deep learning, machine learning-deep learning ensemble), model-driven approaches (mathematical, invariant-based), hybrid datamodel approaches (Physics-Informed Neural Networks), and system-oriented approaches. Our analysis highlights the strengths and weaknesses of each technique, offering a practical guide for creating safer and more reliable systems. By identifying current research gaps, we aim to inspire future work that will enhance the security and adaptability of CPS in our automated world.
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