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

{"title":"A graph embedding-based approach for automatic cyber-physical power system risk assessment to prevent and mitigate threats at scale","authors":"Shining Sun,&nbsp;Hao Huang,&nbsp;Emily Payne,&nbsp;Shamina Hossain-McKenzie,&nbsp;Nicholas Jacobs,&nbsp;H. Vincent Poor,&nbsp;Astrid Layton,&nbsp;Katherine Davis","doi":"10.1049/cps2.12097","DOIUrl":"https://doi.org/10.1049/cps2.12097","url":null,"abstract":"<p>Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large-scale power infrastructures as cyber-physical systems is still in its early stages. Drawing inspiration from machine-learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large-scale cyber-physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high-dimensional structures in low-dimensional spaces, facilitating advanced machine-learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding-based approach, the authors present a structured and methodical framework for risk assessment in cyber-physical systems. The proposed graph embedding-based risk analysis framework aims to provide a more insightful perspective on cyber-physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber-physical power system models: the <i>Western System Coordinating Council (WSCC) 9-Bus System</i> and the <i>Illinois 200-Bus System</i>, thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"435-453"},"PeriodicalIF":1.7,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253023","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":"Joint safety and security risk analysis in industrial cyber-physical systems: A survey","authors":"Zhicong Sun,&nbsp;Guang Chen,&nbsp;Yulong Ding,&nbsp;Shuang-Hua Yang","doi":"10.1049/cps2.12095","DOIUrl":"10.1049/cps2.12095","url":null,"abstract":"<p>Industrial Cyber-Physical Systems (iCPSs) represent a new generation of industrial systems that enable a profound integration of industrial processes and informational spaces, thereby empowering the fourth industrial revolution. iCPSs confront more severe safety and security (S&amp;S) challenges compared to traditional industrial systems. One of the most critical challenges is the joint risk analysis of S&amp;S. Many scholars have devoted their research to this area. However, there is a dearth of literature reviews encapsulating recent advancements, which provides the motivation for this study. The authors review the methodologies in this field, delve into the S&amp;S relationships involved, and propose 12 criteria for evaluating these methods. Furthermore, the current research limitations were analysed and potential directions were suggested for future research.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"334-349"},"PeriodicalIF":1.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343900","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 quantification model for cyber-physical power systems","authors":"Mohammad AlMuhaini","doi":"10.1049/cps2.12098","DOIUrl":"10.1049/cps2.12098","url":null,"abstract":"<p>As power grids develop, their structure becomes more complex, multi-dimensional, and digitalised—hence, it is referred to as cyber-physical infrastructure. The sensitivity of grids to extreme cyber and physical events is becoming a hot research topic due to the increasing rate of such events and their catastrophic consequences. To produce accurate and comprehensive measures, modelling and assessing the resilience of power systems must include both the physical and cyber domains. However, resilience quantification models that include both domains have not received sufficient attention. A novel resilience model and quantification framework are proposed. The model is based on a resilience trapezoid that depicts the different phases of the cyber and physical domains during severe natural or anthropogenic events. A resilience index is also proposed to measure the resilience levels of local nodes and entire systems, including various factors that contribute to the modelled degradation states. Severe weather conditions were modelled to examine the impact of this category of events on the proposed resilience model.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"454-462"},"PeriodicalIF":1.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338829","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-physical system model based on multi-agent system","authors":"Maqbol Ahmed,&nbsp;Okba Kazar,&nbsp;Saad Harous","doi":"10.1049/cps2.12096","DOIUrl":"10.1049/cps2.12096","url":null,"abstract":"<p>Integrating physical processes with computational components creates cyber-physical systems (CPS) that seamlessly interact between the physical and digital worlds. The cyber-physical system has become an interesting research area and an attractive application domain, especially in industry based on the big advantages of this new paradigm. All companies are trying to use this model to control the real industry sector by integrating the cyber system. Many solutions were proposed however, they were not entirely satisfactory. This research proposes a novel CPS model based on Multi-Agent Systems (MAS). This model takes advantage of MAS's collaborative and distributed nature to improve CPS's performance and functionality. Therefore, this model offers a flexible and scalable approach to the development and management of intricate, and interwoven CPSs. The research focuses on developing a CPS model, that encompasses nine layers: the physical agent, security agent, computation agent, decision-making agent, control agent, communication agent, resilience agent, maintenance agent, and application agent layer. The MAS framework is employed to overcome the challenges associated with CPS design, such as coordination, dependability, maintainability, robustness, security, control etc. The results of this exploration are significant in their contribution to the advancement of CPS modelling by utilising Multi-Agent Systems.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"424-434"},"PeriodicalIF":1.7,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352489","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":"Guest Editorial: IoT-based secure health monitoring and tracking through estimated computing","authors":"Rocco Zaccagnino,&nbsp;Arcangelo Castiglione,&nbsp;Marek R. Ogiela,&nbsp;Florin Pop,&nbsp;Weizhi Meng","doi":"10.1049/cps2.12094","DOIUrl":"https://doi.org/10.1049/cps2.12094","url":null,"abstract":"&lt;p&gt;Despite the substantial advancements in health technology, the COVID-19 pandemic has underscored the imperative of enhancing the resilience and efficiency of healthcare systems. Within this context, the Internet of Things (IoT) paradigm emerges as highly pertinent in healthcare services, facilitating enriched doctor-patient interaction while concurrently ameliorating healthcare expenditures. Wearable devices provide patients with personalised access to health-related data, empower physicians with more effective health monitoring capabilities, and enable hospitals to oversee medical equipment, personnel, and infection transmission dynamics. IoT devices, functioning as data aggregators, accumulate extensive datasets, furnishing valuable insights that augment decision-making prowess within healthcare settings. However, the exponential proliferation of IoT devices poses formidable challenges in processing this voluminous and diverse data and extracting actionable insights. Amid the manifold benefits of IoT integration in healthcare services, several hurdles persist, including paramount data security and privacy concerns. Real-time data transmission from IoT devices amplifies these concerns, compounding issues related to data overload and potential inaccuracies. This special issue endeavours to disseminate the latest advancements in IoT within healthcare services. The principal objective is to empower researchers to delve into key concepts conducive to IoT's practical, feasible, and robust integration in healthcare delivery, thereby ensuring expeditious, end-to-end, and dependable service provision to patients.&lt;/p&gt;&lt;p&gt;In this Special Issue, our attention has been directed towards a spectrum of topics of scientific interest, encompassing artificial intelligence and IoT-based healthcare methodologies tailored for pandemic disease management, the synergy between Cloud computing and IoT-based healthcare infrastructures, the intricacies of IoT-based healthcare networks, the application of IoT for personalised health monitoring, the utilisation of IoT for disease diagnosis, and related domains. This special issue aims to showcase the latest research in IoT-based health monitoring systems and estimated computing. The papers presented here will provide valuable insights and contribute to the ongoing efforts to mitigate the impact of pandemics on public health.&lt;/p&gt;&lt;p&gt;The papers selected for this Special Issue collectively demonstrate the progressive advancement of scientific inquiry into solutions for IoT-based Secure Health Monitoring and Tracking through Estimated Computing. The pursuit of synergy among disciplines such as Artificial Intelligence, IoT, and Cloud Computing to develop diagnostic systems for diseases and personalised health monitoring stands poised to emerge as a paramount ambition within the scientific community dedicated to advancing societal well-being and health. Thus, the overall submissions were of high quality, which marks the success ","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"99-101"},"PeriodicalIF":1.5,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304297","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-physical-based welding systems: Components and implementation strategies","authors":"József Szőlősi,&nbsp;Péter Magyar,&nbsp;József Antal,&nbsp;Béla J. Szekeres,&nbsp;Gábor Farkas,&nbsp;Mátyás Andó","doi":"10.1049/cps2.12092","DOIUrl":"10.1049/cps2.12092","url":null,"abstract":"<p>The conditions for a feasible Cyber-Physical System (CPS) in a welding environment are explored for the manufacturing technology components while also focusing on machine learning tools. Increasing manufacturing efficiency means making digitalisation feasible for all technologies, including welding, given today's challenges. Early versions of manufacturing management, such as Computer Integrated Manufacturing, are already leading the way, and one of the latest milestones in these developments is CPS. It can be shown that the digital migration of specific sub-domains (e.g. visual inspection of the weld seam during quality assurance) is significantly more challenging and unimaginable without artificial intelligence applications. However, it is also true that the full integration needed to achieve autonomous manufacturing has yet to be fully achieved, although there is a strong demand in the industry for these CPS to work. In some areas, the digital switchover has already been prepared. However, the interconnection of these subsystems requires modern information systems or, in the case of existing ones, their upgrading to the appropriate level. This research area is set to be addressed comprehensively by initiating several projects. In the initial phase, the aim is to develop an architecture that integrates the various Information Technology applications. In this work, the digital manufacturing environment under CPS is studied, the relevant components are explored, the conditions for the transition from traditional to CPS-based manufacturing are examined and examples of planned further specific studies on the components are listed.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"293-312"},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049823","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":"SEIR-driven semantic integration framework: Internet of Things-enhanced epidemiological surveillance in COVID-19 outbreaks using recurrent neural networks","authors":"Saket Sarin,&nbsp;Sunil K. Singh,&nbsp;Sudhakar Kumar,&nbsp;Shivam Goyal,&nbsp;Brij B. Gupta,&nbsp;Varsha Arya,&nbsp;Kwok Tai Chui","doi":"10.1049/cps2.12091","DOIUrl":"10.1049/cps2.12091","url":null,"abstract":"<p>With the current COVID-19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and complexity of this global emergency. In response to this challenge, the authors present the state-of-the-art SEIR-Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID-19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real-time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID-19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R-squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R-squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"135-149"},"PeriodicalIF":1.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694681","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":"A Petri net model for Time-Delay Attack detection in Precision Time Protocol-based networks","authors":"Mohsen Moradi,&nbsp;Amir Hossein Jahangir","doi":"10.1049/cps2.12088","DOIUrl":"10.1049/cps2.12088","url":null,"abstract":"<p>Along with the development of industrial and distributed systems, security concerns have also emerged in industrial communication protocols. PTP, Precision Time Protocol, is one of the most precise time synchronisation protocols for industrial devices. It ensures real-time activity of the industrial control systems with precision equal to microseconds. In order to address the actual or potential security issues of PTP, this article firstly describes attack models applicable to PTP and then focuses on applying Coloured Petri Net to formally analyse the attack detection methods and also model PTP. The alignment of simulation results with the model and the considered assumptions show the suitability and accuracy of the proposed model.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"407-423"},"PeriodicalIF":1.7,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140426605","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":"Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context","authors":"Yang Liu,&nbsp;Sihui Chen,&nbsp;Peiyi Li,&nbsp;Jiayu Wan,&nbsp;Xin Li","doi":"10.1049/cps2.12086","DOIUrl":"10.1049/cps2.12086","url":null,"abstract":"<p>Energy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber-physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data-driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"207-217"},"PeriodicalIF":1.7,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140471755","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":"Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870-5-104","authors":"M. Agus Syamsul Arifin,&nbsp;Deris Stiawan,&nbsp;Bhakti Yudho Suprapto,&nbsp;Susanto Susanto,&nbsp;Tasmi Salim,&nbsp;Mohd Yazid Idris,&nbsp;Rahmat Budiarto","doi":"10.1049/cps2.12085","DOIUrl":"10.1049/cps2.12085","url":null,"abstract":"<p>Supervisory control and data acquisition systems are critical in Industry 4.0 for controlling and monitoring industrial processes. However, these systems are vulnerable to various attacks, and therefore, intelligent and robust intrusion detection systems as security tools are necessary for ensuring security. Machine learning-based intrusion detection systems require datasets with balanced class distribution, but in practice, imbalanced class distribution is unavoidable. A dataset created by running a supervisory control and data acquisition IEC 60870-5-104 (IEC 104) protocol on a testbed network is presented. The dataset includes normal and attacks traffic data such as port scan, brute force, and Denial of service attacks. Various types of Denial of service attacks are generated to create a robust and specific dataset for training the intrusion detection system model. Three popular techniques for handling class imbalance, that is, random over-sampling, random under-sampling, and synthetic minority oversampling, are implemented to select the best dataset for the experiment. Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. Experimental results indicate that the intrusion detection system model using decision tree and random forest classifiers using random under-sampling achieved the highest accuracy of 99.05%. The intrusion detection system model's performance is verified using various metrics such as recall, precision, F1-Score, receiver operating characteristics curves, and area under the curve. Additionally, 10-fold cross-validation shows no indication of overfitting in the created intrusion detection system model.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"282-292"},"PeriodicalIF":1.7,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386040","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}