Cyber-physical systems (CPS) are considered the integration of physical systems in the real world and control software in computing systems. In CPS, the real world and the computing systems are connected through networks with real-time information exchange. The introduction of networking technologies in CPS has substantial advantages in terms of system efficiency, scalability, maintenance, and many more. However, CPS are vulnerable to malicious attackers intruding on the network. The attackers aim to destroy the physical systems with cyber-physical attacks, in which the main objective is to remotely cause malfunctions in physical systems through networks. Therefore, extensive research on cyber-physical security has been conducted to detect cyber-physical attacks and guarantee the stability of the physical systems under cyber-physical attacks. In this survey, we conduct an exhaustive review of the literature on threats to CPS and on resilient CPS design strategies. First, we discuss the structure of CPS, considering physical dynamics, computing systems, and networks. Then, we provide a taxonomy of cyber-physical attacks with three properties (attack space, attack location, and stealthiness). These three attributes represent the requirements for the implementation of cyber-physical attacks. We review existing studies on anomaly-detection strategies against cyber-physical attacks in terms of physics, networks, and machine learning techniques. Moreover, we analyze the impact of typical cyber-physical attacks and the constraints on attack implementations. In particular, for each of the well-established cyber-physical attacks, we present numerical examples that clearly illustrate the time responses of the physical systems with a conventional physics-based anomaly detector. We also review advances in the typical cyber-physical attacks and the detection methods for these attacks. Furthermore, we review resilient CPS designs in the context of control theory, network management, and data-driven technologies. We conclude the survey by identifying future research directions.

Recently, unmanned aerial vehicles (UAVs), or the so-called drones, have been used in various applications. In particular, UAVs are used for rescue systems, disaster detection, and military purposes, as well as for leisure and commercial purposes. UAVs that are controlled over networks by ground control stations (GCS) can provide various services with expanded activity area. It is thus of critical importance to investigate the vulnerability of the drone system. In this paper, we focus on UAVs controlled by GCS over networks. We analyze the vulnerability of the micro-air-vehicle communication (MAVLink) protocol, which is one of the most widely adopted communication protocols for GCS-based control of UAVs. Then, by exploiting the vulnerability of the MAVLink protocol, we propose an attack methodology that can disable an ongoing mission of a UAV. Our empirical study confirms that the proposed attack can stop the attacked UAV and disable the mission.

Digital Transformation integrates information technology across a broad spectrum of industrial sectors. Industrial Cyber-Physical Systems (ICPS) play a vital role in this transformation by harmonizing machinery, production, logistics, and societal needs through innovative information technologies. This paper investigates the adoption of industrial artificial intelligence (industrial AI) as a methodology for effective ICPS design, introducing AI-Augmented ICPS (AICPS). The study conducts a survey, focusing on the components and interactions of AICPS. We propose design considerations for the implementation of AICPS and investigate the application of cutting-edge industrial AI techniques in each interaction. From the standpoint of AI augmentation, this paper offers insights by identifying key perspectives, including uncertainty of information, safety of AI, explainability of AI, human-societal interactive ICPS, and standardization of industrial AI. This paper aims to enhance understanding of AICPS and lay the groundwork for integrating independent industrial AI techniques into ICPS.

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.