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Industrial control systems in the era of Industry 4.0 present significant challenges from a communication perspective, that is, low latency, ultrahigh reliability, and accurate synchronization. Time-sensitive networking (TSN) has emerged as the main solver of these challenges. As a research trend, TSN and wireless technologies are expected to converge in the wireless TSN paradigm. This convergence starts with the adoption of accurate clock synchronization over wireless systems. In this article, we review the existing wireless clock-synchronization approaches and their attainable performances, and we discuss their feasibility to enable wireless TSN. We conclude that the existing clock-synchronization techniques are enough to enable wireless TSN, although significant implementation efforts are required to incorporate accurate clock synchronization over wireless systems. [ABSTRACT FROM AUTHOR]

Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a preprocessing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and, thus, this article presents a solution to enhance the access classification accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information based authentication using a single sample which includes sample segmentation, grouping, and weighted voting and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices’ sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods. [ABSTRACT FROM AUTHOR]

Industrial wireless cyber–physical systems are vulnerable to malicious node attacks, for example, clone node attack. The existing clone detection schemes are either based on upper layer observations or physical layer channel state information. The schemes based on upper layer observations are vulnerable to defamation while the schemes based on channel state information perform better against defamation, but are badly affected by channel conditions. This article applies physical layer reputation and back propagation neural network to clone detection, aiming at improving the detection accuracy. The proposed scheme accumulates the physical layer reputations by channel state information and input them to the neural network. The cloud server performs attack detection by group detection first. If a certain group is classified as attacked, the corresponding edge processor will perform attack tracing to identify the specific clone nodes. During the attack tracing stage, multiple reputations of each node is adopted for a comprehensive inspection. Extensive experiments are conducted on the Universal Software Radio Peripheral platform. The numerical results show that the proposed scheme significantly improves the detection accuracy. [ABSTRACT FROM AUTHOR]

In this article, a scheme to detect both clone and Sybil attacks by using channel-based machine learning is proposed. To identify malicious attacks, channel responses between sensor peers have been explored as a form of fingerprints with spatial and temporal uniqueness. Moreover, the machine-learning-based method is applied to provide a more accurate authentication rate. Specifically, by combining with edge devices, we apply a threshold detection method based on channel differences to provide offline training sample sets with labels for the machine learning algorithm, which avoids manually generating labels. Therefore, our proposed scheme is lightweight for resource constrained industrial wireless devices, since only an online-decision making is required. Extensive simulations and experiments were conducted in real industrial environments. Both results show that the authentication accuracy rate of our strategy with an appropriate threshold can achieve 84% without manual labeling. [ABSTRACT FROM AUTHOR]

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy. [ABSTRACT FROM AUTHOR]

Wireless networks for industrial control systems are promising because of their reduced cost, flexible structure, and improved long-term reliability. However, wireless control systems are vulnerable to probing-free attacks (PFAs), which are not possible in wired control systems. Thus, wireless control systems must be made as secure as wired systems. Physical (PHY)-layer security technology (PHY-Sec) may be a new strategy for securing industrial wireless control systems. Among all PHY-Sec technologies, PHY-layer authentication is the first step for PHYSec in industrial wireless control systems. This article discusses the principles of PHY-Sec, its application to wireless control systems, and potential research directions. [ABSTRACT FROM AUTHOR]

Motivated by the proliferation of wireless building automation systems (BAS) and increasing security-awareness among BAS operators, in this paper, we propose a taxonomy for the security assessment of BASs. We apply the proposed taxonomy to Thread, an emerging native IP-based protocol for BAS. Our analysis reveals a number of potential weaknesses in the design of Thread. We propose potential solutions for mitigating several identified weaknesses and discuss their efficacy. We also provide suggestions for improvements in future versions of the standard. Overall, our analysis shows that Thread has a well-designed security control for the targeted use case, making it a promising candidate for communication in next generation BASs. [ABSTRACT FROM AUTHOR]