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

Industrial robotic systems in the era of Industry 4.0 play a pivotal role in modern manufacturing. These systems, which belong to the larger class of cyber-physical systems (CPSs), rely heavily on advanced sensing capabilities to execute complex and delicate tasks with high precision and efficiency. It is of no surprise that the integration of sensors with Industry 4.0 robotic systems exposes them to potential cyber-physical risks/threats. This paper addresses a critical gap in the literature of industrial robotics cybersecurity by presenting a comprehensive analysis of vulnerabilities in the sensing systems of industrial robots. In particular, we systematically explore how sensor performance limits, faults and biases can be exploited by attackers who can then turn these inherent weaknesses into security threats. Our investigation relies on a detailed literature review of a multitude of commonly used sensors in industrial robotic systems through the lens of their physics-based operating principles, classifications, performance limits, potential faults and associated vulnerabilities against disturbances such as temperature fluctuations, electromagnetic and acoustic interference, and ambient light variations. The result of this systematic investigation is a ring chart illustrating the overlaps and entanglements of sensor faults and performance limits, which can be exploited by cyber-physical adversaries. Additionally, we investigate the cascading effects of compromised sensor data on the operation of industrial robotic systems through a cause-and-effect analysis, where the sensor vulnerabilities can cause malfunction and lead to cyber-physical damage. The result of this analysis is a sensor cyber-physical threat cause-and-effect diagram, which can be employed for design of robust and effective cyber-physical defence measures. By providing insights into sensor-related cyber-risks, our cyber-physical threat analysis paves the path for enhanced industrial robotics security.

The rapid expansion of electric vehicle (EV) charging infrastructure has introduced significant vulnerabilities to cyber-physical threats, raising concerns about the resilience of both charging and smart power grid systems. This paper presents an innovative investigation into the resilience of EV charging infrastructure using a real-time co-simulation testbed, integrating both power network models and communication protocols such as IEC 61850. The study addresses gaps in existing research by implementing a realistic smart grid environment that incorporates EVs, charging stations and communication networks to simulate cyber-physical interactions. Key cyber-attacks, such as remote charging station status and configuration manipulations and their impact on it, are analysed in real-time simulations. Results show that even a relatively small attack utilising an IEEE 9-bus system with two EV charging stations can severely disrupt grid stability. The paper also explores various attacks targeting EV infrastructure, including charging stations, communication protocols, and management systems. The combined effects of cyber-attacks on power consumption and current variation highlight the critical importance of ensuring that charging infrastructure can adapt to sudden changes in demand while maintaining operational integrity.

Designing the next-generation complex distributed cyber-physical systems (dCPS) poses significant challenges for manufacturing companies, necessitating efficient design space exploration (DSE) techniques to evaluate potential design decisions and their impact on nonfunctional aspects of the systems, such as performance, reliability and energy consumption. This paper introduces CompDSE, a methodology designed to facilitate the DSE of complex dCPS, specifically focusing on the cyber components, that is, the computing subsystems within dCPS. CompDSE defines and utilises abstract models of the application workload, computing hardware platform and workload-to-platform mapping of dCPS, automatically derived from runtime trace data, and integrates them into a discrete event simulation environment to explore various design points. We demonstrate the effectiveness of our methodology through a case study on the ASML TWINSCAN lithography machine, a complex industrial dCPS. The results showcase potential performance enhancements achieved by optimising computing subsystems while considering physical constraints. Evaluating each design point takes under a minute, highlighting the CompDSE efficiency and scalability in tackling complex dCPS with large design spaces.

Smart advanced metering infrastructure and edge devices show promising solutions in digitalising distributed energy systems. Energy disaggregation of household load consumption provides a better understanding of consumers’ appliance-level usage patterns. Machine learning approaches enhance the power system's efficiency but this is contingent upon sufficient training samples for efficient and accurate prediction tasks. In a centralised setup, transferring such a substantially high volume of information to the cloud server has a communication bottleneck. Although high-computing edge devices seek to address such problems, the data scarcity and heterogeneity among clients remain challenges to be addressed. Federated learning offers a compelling solution in such a scenario by leveraging the ML model training at edge devices and aggregating the client's updates at a cloud server. However, FL still faces significant security issues, including the potential eavesdropping by a malicious actor with the intention of stealing clients' information while communicating with an honest-but-curious server. The study aims to secure the sensitive information of energy users participating in the nonintrusive load monitoring (NILM) program by integrating differential privacy with a personalised federated learning approach. The Fisher information method was adapted to extract the global model information based on common features, while personalised updates will not be shared with the server for client-specific features. Similarly, the authors employed an adaptive differential privacy only on the shared local updates (DP-PFL) while communicating with the server. Experimental results on the Pecan Street and REFIT datasets depict that DP-PFL exhibits more favourable performance on both the energy prediction and status classification tasks compared to other state-of-the-art DP approaches in federated NILM.

Socio-Technical Security Modelling and Simulation (STSec-M&S) is a technique used for reasoning and representing security viewpoints that include both the social and technical aspects of a system. It has shown great potential for improving the cybersecurity and resilience of Critical Infrastructure (CI). This study involved a multi-methods approach, consisting of a scoping literature review and a focus group workshop, conducted with stakeholder engagement from critical infrastructure stakeholders to explore their perceptions and practices regarding the use of socio-technical security modelling and simulation. The findings suggest that the current state of knowledge regarding the use and effectiveness of STSec-M&Ss approaches is limited in CI domains. Consequently, there is little application of it in existing CI systems, regardless of its recognised benefits of enabling a better understanding of CI functionalities, security goals, early and more holistic risk identifications and selection of appropriate countermeasures. The benefits of the STSec-M&S approach can be better realised by effective cross-sector communications and collaborations, team partnerships, system and approach sophistication, and better security awareness amongst others. The potential barriers that can impede such benefits include high expense for implementing the technique, low data availability and quality, regulatory compliance, and competency gaps etc. Helpful recommendations include exploring and using realistic data, validating system security models, and exploring new ways of reskilling and upskilling CI stakeholders in socio-technical security-thinking and M&S approaches to enhance cybersecurity and resilience of CIs.

Offshore microgrids such as oil and gas platforms are embracing wind power to reduce onsite gas consumption and carbon emission. Meanwhile, the intermittency of wind power threats the operational security of offshore microgrids which are mainly islanded cyber-physical system. Although energy storage system (ESS) could smooth the wind power, it also changes the operational strategy of the microgrids. Yet, it is still not clear on how to determine the ESS configuration, particularly for MW-level offshore microgrid with limited rooms for ESS installment. In this paper, an optimal ESS configuration method is proposed to support operational scheduling and frequency regulation of the microgrids at different time scales. A source-storage-load coordinated frequency response model is proposed to exploit the advantages of different types of ESS. The model is converted to convex quadratic forms and incorporated into the ESS configuration model to guarantee the frequency stability of offshore microgrids. The proposed ESS configuration method is validated using the data of a real offshore oil and gas platform. Compared with existing methods, the full life cycle economic efficiency, wind power utilisation, and operational security are all significantly improved.

This paper presents a synchrophasor-based real-time cyber-physical power system testbed with a novel security evaluation tool, pySynphasor, that can emulate different real attack scenarios on the phasor measurement unit (PMU). The testbed focuses on real-time cyber-security emulation using different components, including a real-time digital simulator, virtual machines (VM), a communication network emulator, and a packet manipulation tool. The script-based VM deployment and software-defined network emulation facilitate a highly scalable cyber-physical testbed, which enables emulations of a real power system under different attack scenarios such as address resolution protocol (ARP) poisoning attack, man-in-the-middle (MITM) attack, false data injection attack (FDIA), and eavesdropping attack. An open-source pySynphasor module has been implemented to analyse the security vulnerabilities of the IEEE C37.118.2 protocol. The paper also presents an interactive framework for injecting false data into a realistic system utilising the pySynphasor module, which can dissect and reconstruct the C37.118.2 packets. Therefore, it expands the potential of testing and developing PMU-based systems and analysing their security vulnerabilities, benefiting the power industry and academia. A case study demonstrating the FDIA attack on the PMU measurements and the bad-data detection technique is presented as an example of the testbed capability.

Traditional security measures such as access control and authentication need to be more effective against ever-evolving threats. Moreover, security concerns increase as more industries shift towards adopting the industrial Internet of things (IIoT). Therefore, this paper proposes secure measures using deep machine learning-based intrusion detection and advanced encryption schemes based on lattice-based cryptography on three-layered cloud-edge-fog IIoT architecture. The novelty of the paper is an integrated security framework for IIoT that combines deep learning-based intrusion detection system (IDS) with lightweight cryptographic protocols. For deep learning, multi-layer perception (MLP), convolutional neural network (CNN), and TabNet were implemented for intruder detection systems from edge to cloud layer, and ring learning with error (RLWE) was proposed for homomorphic encryption to communicate data between fog and edge layer. The evaluation experiments were performed on the Ton_IoT dataset and the results show that the deep learning models have a very good accuracy of around 92% for multiclass attack classification. Moreover, RLWE results show improved encryption time and reduced ciphertext size against standard Learning With Error encryption.

Smart grid operators use load forecasting algorithms to predict energy load for the reliable and economical operation of the electricity grid. COVID-19 pandemic-like situations (PLS) can significantly impact energy load demand due to uncertainties in factors such as regulatory orders, pandemic severity and human behavioural patterns. Additionally, in a smart grid, cyberattacks can manipulate forecasted load data, leading to suboptimal decisions, economic losses and potential blackouts. Forecasting load during these situations is challenging for traditional load forecasting tools, as they struggle to identify cyberattacks amidst uncertain load demand, where cyberattacks may mimic pandemic-like load patterns. Traditional forecasting methods do not incorporate factors related to pandemics and cyberattacks. Recent studies have focused on forecasting by considering factors such as COVID-19 cases, social distancing, weather, and temperature but fail to account for the impact of regulatory orders and pandemic severity. They also lack the ability to differentiate between normal and anomalous forecasts and classify the type of attack in anomalous data. This paper presents a tool for short-term load forecasting, anomaly detection and cyberattack classification for pandemic-like situations (PLS). The proposed short-term load forecasting algorithm uses a weighted moving average and an adjustment factor incorporating regulatory orders and pandemic severity, making it computationally efficient and deterministic. Additionally, the proposed anomaly detection and cyberattack classification algorithm provides robust options for detecting anomalies and classifying various types of cyberattacks. The proposed tool has been evaluated using K-Fold cross-validation to improve generalisability and reduce overfitting. The mean squared error (MSE) was used to measure prediction accuracy and detect discrepancies. It has been analysed and tested on real-load data from the State Load Dispatch Centre (SLDC), Delhi, of the Indian National Grid.

Modern control systems integrate with information technologies through Networked Control Systems and Cyber-Physical Systems (CPS). Although these systems are beneficial, they raise security concerns for critical infrastructure. Cyberattacks on CPS communication channels, such as denial-of-service (DoS) attacks, can cause significant time delays and data loss, leading to poor system performance and instability. This article assumes weak DoS attack influences as an unknown delay. Then, system maximum resistance time against DoS attacks will be calculated according to the Lyapunov–Krasovskii theorem, and a conservative upper bound delay is included in the system model, which maintains system stability. With this assumption, Kharitonov's theorem-based robust Proportional-Integral (PI) controller is developed to mitigate DoS attacks. In addition, another Ziegler–Nichols tuned PI controller is presented to demonstrate that the proposed robust PI controller effectively reduces DoS attack impacts on CPSs. Finally, in a liquid-level networked control system, the efficacy of two PI controllers was evaluated. Results show that Kharitonov's theorem-based controller surpasses the Ziegler–Nichols method PI controller in mitigating the impact of DoS attacks on system behaviour, including maintaining system stability and keeping both transient response characteristics and setpoint tracking at desired values. Also, the proposed design strategy for reducing DoS attack effects is simple and less conservative than other robust control methods.