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":"A survey on detection and localisation of false data injection attacks in smart grids","authors":"Muhammad Irfan,&nbsp;Alireza Sadighian,&nbsp;Adeen Tanveer,&nbsp;Shaikha J. Al-Naimi,&nbsp;Gabriele Oligeri","doi":"10.1049/cps2.12093","DOIUrl":"https://doi.org/10.1049/cps2.12093","url":null,"abstract":"<p>In the recent years, cyberattacks to smart grids are becoming more frequent. Among the many malicious activities that can be launched against smart grids, the False Data Injection (FDI) attacks have raised significant concerns from both academia and industry. FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. Hence, prompt detection and precise localisation of FDI attacks are becoming of paramount importance to ensure smart grids security and safety. Several papers recently started to study and analyse this topic from different perspectives and address existing challenges. Data-driven techniques and mathematical modelling are the major ingredients of the proposed approaches. The primary objective is to provide a systematic review and insights into FDI attacks joint detection and localisation approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localisation aspects. For this purpose, more than 40 major research contributions were selected and inspected, while conducting a detailed analysis of the methodology and objectives in relation to the FDI attacks detection and localisation. Key findings of the identified papers were provided according to different criteria, such as employed FDI attacks localisation techniques, utilised evaluation scenarios, investigated FDI attack types, application scenarios, adopted methodologies and the use of additional data. Finally, open issues and future research directions were discussed.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"313-333"},"PeriodicalIF":1.7,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253483","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 machine learning model for Alzheimer's disease prediction","authors":"Pooja Rani,&nbsp;Rohit Lamba,&nbsp;Ravi Kumar Sachdeva,&nbsp;Karan Kumar,&nbsp;Celestine Iwendi","doi":"10.1049/cps2.12090","DOIUrl":"10.1049/cps2.12090","url":null,"abstract":"<p>Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. A methodology SMOTE-RF is proposed for AD prediction. Alzheimer's is predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies longitudinal dataset available on Kaggle is used for experiments. The dataset is balanced using synthetic minority oversampling technique. Experiments are done on both imbalanced and balanced datasets. Decision tree obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum of 87.84% accuracy on the imbalanced dataset. Decision tree obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. A maximum accuracy of 95.03% is achieved with SMOTE-RF.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"125-134"},"PeriodicalIF":1.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227957","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":"Securing the Internet of Medical Things with ECG-based PUF encryption","authors":"Biagio Boi,&nbsp;Christian Esposito","doi":"10.1049/cps2.12089","DOIUrl":"10.1049/cps2.12089","url":null,"abstract":"<p>The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditional protection means due to poor battery equipment and limited storage and computational capabilities of IoT devices. The authors analyze techniques applied in the medical context to encrypt sensible data and deal with the unique challenges of resource-constrained devices. A technique that is facing increasing interest is the Physical Unclonable Function (PUF), where biometrics are implemented on integrated circuits' electric features. PUFs, however, demand special hardware, so in this work, instead of considering the physical device as a source of randomness, an ElectroCardioGram (ECG) can be taken into consideration to make a ‘virtual’ PUF. Such an mechanism leverages individual ECG signals to generate a cryptographic key for encrypting and decrypting data. Due to the poor stability of the ECG signal and the typical noise existing in the measurement process for such a signal, filtering and feature extraction techniques must be adopted. The proposed model considers the adoption of pre-processing techniques in conjunction with a fuzzy extractor to add stability to the signal. Experiments were performed on a dataset containing ECG records gathered over 6 months, yielding good results in the short term and valuable outcomes in the long term, paving the way for adaptive PUF techniques in this context.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"115-124"},"PeriodicalIF":1.5,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257509","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}