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.

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.

Cyber-physical attacks exploit intrinsic natures of physical systems and can severely damage cyber-physical systems (CPSs) without being detected by the conventional anomaly detector. In this article, based on software-defined networking, we propose a holistic resilient CPS framework that can detect, isolate, and recover from cyber-physical attacks in real time. To show the effectiveness of the proposed framework, we focus on the pole-dynamics attack (PDA), a newly reported stealthy sensor attack that can make the physical system unstable. We develop an efficient detection algorithm for PDA and embed it into the proposed framework. By implementing a testbed, we validate that the proposed framework guarantees resilience of CPS against the PDA.

A cyber-physical system (CPS) is an entanglement of the physical and the computing systems by real-time information exchange through networking, which can be considered as real-time IoT because of end-to-end real-time performance guarantee. Most of societal infrastructures such as transportation systems, smart power grid, smart factory, and smart buildings are key application domains of CPS. Though there have been extensive studies on infrastructures from the perspective of cyber security, insufficient research has been conducted from a practical viewpoint of cyber-physical security. In this paper, we focus on train control systems as one of the critical infrastructures. We fully investigate the emerging de facto standard of train control systems, communication based train control (CBTC). We analyze the cyber-physical vulnerability of CBTC and discover that a man-in-the-middle attack combined with knowledge on train signaling can cause train collisions in CBTC. To resolve the issue, we propose a countermeasure for resiliency of CBTC. By implementing a realistic CBTC testbed, we validate our analysis. To the best of our knowledge, this is the first in-depth empirical study on cyber-physical vulnerability of CBTC systems.

Networks on the cyber-physical systems (CPSs) configure feedback control loops between physical systems in the real world and control software in the cyber world. Malicious behaviors on the networks can increase network delays by exhausting limited network resources and security vulnerabilities to destabilize CPSs, which are entitled the network delay attack. In this paper, we focus on the problem of how to guarantee the stability of CPS under the network delay attack. We propose a real-time controller reconfiguration to ensure the resiliency of the physical systems against the network delay attack. Our controller reconfiguration consists of two algorithms: controller gain tuning and access point (AP) handover, which give a delay tolerance and an attack avoidance, respectively. Depending on the network delays, the computing system adopts one of these two algorithms and mitigates the physical impacts of the network delay attack. We validate that the proposed controller reconfiguration can ensure the resiliency of CPS against the network delay attack by implementing a testbed with wireless networks.

Controller failures can result in unsafe physical plant operations and deteriorated performance in industrial cyber–physical systems. In this paper, we present a controller switching mechanism over wireless sensor–actuator networks to enhance the resiliency of control systems against problems and potential physical failures. The proposed mechanism detects controller failures and quickly switches to the backup controller to ensure the stability of the control system in case the primary controller fails. To show the efficacy of our proposed method, we conduct a performance evaluation using a hardware-in-the-loop testbed that considers both the actual wireless network protocol and the simulated physical system. Results demonstrate that the proposed scheme recovers quickly by switching to a backup controller in the case of controller failure.