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.

翻译：在日益互联的世界中，网络物理系统对于医疗、交通和制造等关键行业至关重要，它将物理过程与计算智能融为一体。然而，这些系统的安全性是一个主要问题。无论是传感器故障还是网络攻击引起的异常，都可能导致灾难性故障，因此有效的检测对于防止损害和服务中断至关重要。本文对网络物理系统中的异常检测技术进行了全面综述。我们对各种方法进行了分类和比较，包括数据驱动方法（机器学习、深度学习、机器学习-深度学习集成）、模型驱动方法（数学方法、基于不变量的方法）、混合数据模型方法（物理信息神经网络）以及面向系统的方法。我们的分析强调了每种技术的优缺点，为创建更安全、更可靠的系统提供了实用指南。通过确定当前的研究空白，我们旨在激发未来的工作，以增强自动化世界中网络物理系统的安全性和适应性。