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

{"title":"Socio-Technical Security Modelling and Simulations in Cyber-Physical Systems: Outlook on Knowledge, Perceptions, Practices, Enablers, and Barriers","authors":"Uchenna Daniel Ani,&nbsp;Mohammed Al-Mhiqani,&nbsp;Nilufer Tuptuk,&nbsp;Stephen Hailes,&nbsp;Jeremy Daniel McKendrick Watson","doi":"10.1049/cps2.70017","DOIUrl":"https://doi.org/10.1049/cps2.70017","url":null,"abstract":"<p>Socio-Technical Security Modelling and Simulation (STSec-M&amp;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&amp;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&amp;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&amp;S approaches to enhance cybersecurity and resilience of CIs.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy Storage System Configuration for Supporting the Scheduling and Frequency Regulation of Offshore Microgrids","authors":"Longfei Liu,&nbsp;Jing Liu,&nbsp;Xiandong Xu,&nbsp;Xiaodan Yu,&nbsp;Wei Wei,&nbsp;Hongjie Jia","doi":"10.1049/cps2.70010","DOIUrl":"https://doi.org/10.1049/cps2.70010","url":null,"abstract":"<p>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.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable cyber-physical testbed for cybersecurity evaluation of synchrophasors in power systems","authors":"Shuvangkar Chandra Das,&nbsp;Tuyen Vu,&nbsp;Herbert Ginn","doi":"10.1049/cps2.12106","DOIUrl":"https://doi.org/10.1049/cps2.12106","url":null,"abstract":"<p>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.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CIDER: Cyber-Security in Industrial IoT Using Deep Learning and Ring Learning with Errors","authors":"Siu Ting Tsoi,&nbsp;Anish Jindal","doi":"10.1049/cps2.70015","DOIUrl":"https://doi.org/10.1049/cps2.70015","url":null,"abstract":"<p>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.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyber Risk Identification and Classification-Based Load Forecasting Tool for Pandemic Situations","authors":"Kuldeep Singh Shivran,&nbsp;Kyle Swire-Thompson,&nbsp;Neetesh Saxena,&nbsp;Sarasij Das","doi":"10.1049/cps2.70014","DOIUrl":"https://doi.org/10.1049/cps2.70014","url":null,"abstract":"<p>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.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience PI controller design for mitigating weak denial-of-service attacks in cyber-physical systems","authors":"Hamid Reza Chavoshi,&nbsp;Ali Khoshlahjeh Sedgh,&nbsp;Hamid Khaloozadeh","doi":"10.1049/cps2.70002","DOIUrl":"https://doi.org/10.1049/cps2.70002","url":null,"abstract":"<p>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.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Dimensionality Reduction Based on Deep Lasso for Wind Power Forecasting","authors":"Haohan Liao,&nbsp;Kunming Fu,&nbsp;Shiji Pan,&nbsp;Yongning Zhao","doi":"10.1049/cps2.70011","DOIUrl":"https://doi.org/10.1049/cps2.70011","url":null,"abstract":"<p>Wind power forecasting considering spatio-temporal correlations can effectively improve the forecasting accuracy. However, this will lead to a complicated structure in the forecasting model, making it difficult to solve due to dimensional catastrophe. To this end, a neural network framework called Deep Lasso is applied, which achieves feature selection by adding the regularisation of Lasso to the input gradients. Primarily, a forecasting model based on Deep Lasso, considering the features of all wind farms (i.e., global variables), is constructed. Subsequently, the coefficients of Deep Lasso can directly represent the contribution of input features to wind power forecasts. Therefore, to construct a more efficient forecasting model, secondary modelling is performed by filtering the features with small coefficients. Experiments including 20 wind farms demonstrate that Deep Lasso exhibits remarkable suitability and effectiveness in ultra-short-term wind power forecasting compared with six feature selection methods. Moreover, to test the effectiveness of feature dimensionality reduction, the secondary modelling forecasting model is verified by comparing it with principal component analysis (PCA) and factor analysis (FA). The results obtained show that the overall performance of the proposed method outperforms PCA and FA while improving the computational efficiency to a certain extent.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Performance in Mixed-Criticality Real-Time Systems Through Learner-Based Resource Management","authors":"Mohammadreza Saberikia,&nbsp;Hakem Beitollahi,&nbsp;Rasool Jader,&nbsp;Hamed Farbeh","doi":"10.1049/cps2.70007","DOIUrl":"https://doi.org/10.1049/cps2.70007","url":null,"abstract":"<p>In mixed-criticality (MC) systems, tasks with varying criticality levels share resources, leading to challenges in resource management during mode transitions. Existing approaches often result in suboptimal performance due to resource contention and criticality level inheritance. This paper introduces a novel learner-based resource management strategy that predicts optimal mode switching times and prevents low-criticality tasks from acquiring resources during critical periods. By combining vector autoregressive (VAR) and feed-forward neural network (FNN) techniques, our approach effectively anticipates system state changes and optimises resource allocation. Specifically, the method extracts key system features, including processor temperature, soft error rate, cache miss rate, and task slack time. A hybrid forecasting model then predicts the probability of a mode transition within a specified time horizon. Based on these predictions, the system proactively denies resource requests from low-criticality tasks during periods of high probability of mode transition, ensuring the availability of resources for high-criticality tasks. Comprehensive simulations demonstrate significant reductions in blocking time (up to 75%), miss rate (up to 9.35%), and energy consumption (up to 12.15%) compared to state-of-the-art methods. These improvements enhance system reliability and efficiency, making it suitable for safety-critical applications.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative Optimisation of Carbon Trading Mechanism and Heat Network in Integrated Energy System","authors":"Zhiguo Dong,&nbsp;Li Wang,&nbsp;Fengxiang Xie,&nbsp;Yongcheng Yu,&nbsp;Runjie Li,&nbsp;Luyong Cao","doi":"10.1049/cps2.70009","DOIUrl":"https://doi.org/10.1049/cps2.70009","url":null,"abstract":"<p>To achieve low-carbon development, the ladder-type carbon trading mechanism is proved to be beneficial to reduce carbon emissions while increasing the operation cost of the integrated energy system (IES). In this paper, an IES optimal operation strategy considering the ladder-type carbon trading mechanism is proposed, with the help of the dynamic characteristics of the heat network to compensate for the increased operation cost. First, the transmission model of the heat network is established, and the dynamic characteristic of the heat network during the heat transfer process is analysed Then, the ladder-type carbon trading mechanism is introduced, and the impact on IES operation is analysed accordingly. Finally, the IES optimal operation model considering the ladder-type carbon trading mechanism and the dynamic characteristics of the heat network is established. The programming model is expressed as mixed-integer quadratic programming (MIQP). Simulation experiments are carried out for validation. The results show that considering the ladder-type carbon trading mechanism and the dynamic characteristics of the heat network in the IES can improve the wind power consumption rate and reduce the system operation cost and carbon emissions.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Damping Characteristics in Wind Turbine-Energy Storage Hybrid Systems Based on Path Module","authors":"Shanshan Cheng,&nbsp;Haixin Wang,&nbsp;Jing Li,&nbsp;Shengyang Lu,&nbsp;Xinyi Lu,&nbsp;Junyou Yang,&nbsp;Zhe Chen","doi":"10.1049/cps2.70006","DOIUrl":"https://doi.org/10.1049/cps2.70006","url":null,"abstract":"<p>Current analytical methods are inadequate in uncovering the internal propagation mechanisms of disturbances and the interconnections between subsystems in the wind turbine-storage integrated grid connected system, which faces stability issues. Therefore, this paper employs a damping module modelling approach to conduct a dynamic analysis of the dynamic interactions in wind turbine-storage storage integrated systems, focusing on the damping path analysis with the phase-locked loop (PLL) as the oscillation mode. The research initiates with the linearisation of the doubly-fed induction generator (DFIG) and energy storage system (ESS) models. The closed-loop structure of the system is then presented to expose the disturbance propagation paths between subsystems. Subsequently, the damping coefficients of the second-order dynamic equation are expanded to include the dynamic equations of the most prominent oscillation mode, which establishes stability criteria for the system. Finally, by performing damping decomposition and reconstruction, the damping coefficients of each subsystem as well as the total damping coefficient of the interconnection system are obtained. An analysis is conducted on how the proportional-integral parameters of the PLL affect the damping of the interconnection system. The results suggest that the damping paths of the DFIG and the ESS can be expressed as a closed-loop structure diagram. By decreasing the proportional or integral coefficients of the PLL, the overall damping coefficient is increased, resulting in an enhancement of the stability of the grid-connected system.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}