IET Cyber-Physical Systems: Theory and Applications最新文献

Time-Sensitive Networking (TSN) extends IEEE 802.1 Ethernet for safety-critical and real-time applications in several areas, for example, automotive, aerospace or industrial automation. However, many of these systems also have stringent security requirements, and security attacks may impair safety. Given a TSN-based distributed architecture, a set of applications with tasks and messages as well as a set of security and redundancy requirements, the authors are interested to synthesise a system configuration such that the real-time, safety and security requirements are upheld. The Timed Efficient Stream Loss-Tolerant Authentication (TESLA) low-resource multicast authentication protocol is used to guarantee the security requirements and redundant disjunct message routes to tolerate link failures. The authors consider that tasks are dispatched using a static cyclic schedule table and that the messages use the time-sensitive traffic class in TSN, which relies on schedule tables (called Gate Control Lists, GCLs) in the network switches. A configuration consists of the schedule tables for tasks as well as the disjoint routes and GCLs for messages. A Constraint Programing-based formulation, which can be used to find an optimal solution with respect to the cost function, is proposed. Additionally, a Simulated Annealing-based metaheuristic, which can find good solution for large test cases, is proposed. The authors evaluate both approaches on several test cases.

Information and communication technologies have increasingly been used to support the exchange of measurements and control signals in industrial control systems, making them important applications of cyber-physical industrial control systems (CPICSs) such as electrical power systems and intelligent transportation systems. While the communication infrastructure significantly facilitates the transmission of vast amounts of data over wide geographical areas, it makes CPICSs vulnerable to cyber-attacks; protecting CPICSs of critical infrastructures from cyber-attacks is crucial and challenging. In order to secure CPICSs, a variety of open challenges need to be tackled, including cyber-physical system modelling approaches, advanced intrusion detection systems, and resilient estimation and control methods. Machine learning (ML) and its emerging algorithms offer the potential of dealing with large-scale data analysis, data processing and decision-making in the security of CPICSs.

This special issue publishes state-of-the-art ML-based solutions for the open challenges in securing CPICSs of critical infrastructures.

When modelling cyber-attacks in CPICSs, most of existing works consider using external disturbances, which follow certain assumptions. While it is not sufficient to model cyber-attacks simply as disturbances, the paper ‘Game theoretic vulnerability management for secondary frequency control of islanded microgrids against false data injection (FDI) attacks’ by S. Liu et al. considers the dynamic interaction between the smart attacker (the spoofer) and the defender the microgrid control centre (MGCC). The authors propose a stochastic game between the MGCC and the attacker for enhancing the vulnerability of the MGCC to FDI attack (wireless spoof attack).

As communication networks are implemented for information exchange between the master and slave sides of bilateral teleoperation systems, they are also exposed to cyber-attack threats. The paper ‘Mode-dependent switching control of bilateral teleoperation against random denial-of-service attacks’ by L. Hu et al. analyses the performance of bilateral teleoperation systems in the presence of random denial-of-service (DoS) attacks and constant transmission delays and proposes a mode-dependent switching controller to mitigate the influence of DoS attacks.

While machine-learning algorithms are helpful in identifying cyber-attacks such as network intrusion, common network intrusion datasets are negatively affected by class imbalance; the normal traffic behaviour constitutes most of the dataset, whereas intrusion traffic behaviour forms a significantly smaller portion. The paper ‘Network intrusion detection using ML approaches: Addressing data imbalance’ by R. Ahsan et al. conducts a comparative evaluation on the impact of data imbalance of various ML algorithms and presents a hybrid voting classifier to improve the results.

To improve the anomaly detection performance w

Cyber-physical systems (CPS) integrate control, sensing, and processing into interconnected physical components to support applications within transportation, energy, healthcare, environment, and various other areas. Secure and reliable wireless communication between devices is necessary to enable the widespread adoption of these emerging technologies. Cyber-physical systems devices must be protected against active threats, such as Radio Frequency (RF) Jammers, which intentionally disrupt communication links. Jamming detection and mitigation techniques must be evaluated extensively to validate algorithms prior to full implementation. Challenges related to obtaining zoning permits, Federal Aviation Administration (FAA) pilot certification for Unmanned Aerial Vehicles (UAVs), and Federal Communications Commission (FCC) licencing lead to evaluation limited to simulation-based or simplistic, non-representative hardware experimentation. A site-specific ray-tracing emulation framework is presented to provide a realistic evaluation of communication devices under RF jamming attacks in complex scenarios involving mobility, vehicular, and UAV systems. System architecture and capabilities are provided for the devices under test, real-world jamming adversaries, channel modelling, and channel emulation. Case studies are provided to demonstrate the use of the framework for different applications and jamming threats. The experimental results illustrate the benefit of the ray-tracing emulation system for conducting complex wireless communication studies under the presence of RF jamming.

These days, networked control systems (NCSs) in which data is transmitted via communication have been actively studied for many potential applications. In an NCS, data dropout degrades control performance depending on network conditions. For an NCS with data dropout, the authors propose a model-predictive-control-based input optimisation, representing data dropout as both a Bernoulli model and a finite-order Markov chain. Using the proposed NCS data dropout model, the authors derive an optimal input that provides the estimated error between the expected state of the plant and a given reference. The proposed control problem is formulated as its equivalent quadratic programming, as executed at each online sampling. The authors also demonstrate simulations and experiments to show the effectiveness of the proposed method.

Networked control systems have received increasing attention from many researchers because of their vast potential. The insertion of communication networks in a controlled system brings network-induced defects, which are mainly caused by limited network resources. This paper proposes a codesign of periodic communication scheduling and a controller using sparsity for efficient use of the network while improving initial control performance. The effectiveness of the proposed method is verified through two numerical simulations.

The recent development of the communication technology accelerates studies of real-time networked control systems using networks. The data dropout is essentially unavoidable, especially in wireless networks and it results from transmission errors and network traffic congestion. Multiple time-varying network traffic status given by discrete-time homogeneous Markov chains is assumed. The authors estimate the network traffic status characterised by the probability matrix of the Markov chain online from the data dropout history. According to the estimation of network traffic status, an appropriate controller is selected to improve the control performance. The effectiveness of the proposed method is verified through simulations and experiments.

Nowadays, the reliable supply of electric power is vital in all aspects of social life. With the development and participation of distributed generations, not only does an accurate fault location lets repair of a fault line as quickly as possible, but also it is of great significance to ensure the safe and stable economic operation of the power system. This study proposes a method to determine the fault location in distribution networks, which is a combination of Extreme Gradient Boosting and Support Vector Machine. The effectiveness of the proposed method is validated on an IEEE34-bus distribution network under single-phase-to-ground faults, using voltage measurements available at each node in the distribution network. The comparison in accuracy, precision, recall, F1-score and time-cost of the method in this study with K-Nearest Neighbour and Multi-Layer Perceptron demonstrates the feasibility of applying the proposed method in distribution system fault diagnosis.

In this work, the problem of anomaly detection in imbalanced datasets, framed in the context of network intrusion detection is studied. A novel anomaly detection solution that takes both data-level and algorithm-level approaches into account to cope with the class-imbalance problem is proposed. This solution integrates the auto-learning ability of Reinforcement Learning with the oversampling ability of a Conditional Generative Adversarial Network (CGAN). To further investigate the potential of a CGAN, in imbalanced classification tasks, the effect of CGAN-based oversampling on the following classifiers is examined: Naïve Bayes, Multilayer Perceptron, Random Forest and Logistic Regression. Through the experimental results, the authors demonstrate improved performance from the proposed approach, and from CGAN-based oversampling in general, over other oversampling techniques such as Synthetic Minority Oversampling Technique and Adaptive Synthetic.

Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0–1 encoding method and Hash-based Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.

Active distribution network (ADN) technology, as an important trend of the future smart distribution grid, is able to effectively absorb distributed energy resource (DER), to reasonably optimise grid-load operation characteristics, and to safely support the reliability of power supply. Through enhancing energy utilisation efficiency and friendly interaction with user access, ADN technology is also able to comprehensively improve the power supply reliability of the distribution network. However, distributed feeder automation (FA), as an important part of ADN technology, will also meet new problems and challenges with the access of DER in the distribution network. The formal method can analyse the correctness and effectiveness of a distributed fault processing algorithm from mathematical logic, which provides an important theoretical basis for distributed fault processing. The focus herein is on the formal description and verification of topology modelling in fault location, isolation, and service restoration (FLISR) based on distributed processing. By abstracting and simplifying the complex power system features, the adaptability of the formal method is solved. The logical correctness of the topology model in FLISR based on distributed processing is verified. Finally, the distributed local topology model and algorithm is verified through a formal method using an actual ADN example.