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

{"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}

{"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":"Leveraging graph clustering techniques for cyber-physical system analysis to enhance disturbance characterisation","authors":"Nicholas Jacobs,&nbsp;Shamina Hossain-McKenzie,&nbsp;Shining Sun,&nbsp;Emily Payne,&nbsp;Adam Summers,&nbsp;Leen Al-Homoud,&nbsp;Astrid Layton,&nbsp;Kate Davis,&nbsp;Chris Goes","doi":"10.1049/cps2.12087","DOIUrl":"10.1049/cps2.12087","url":null,"abstract":"<p>Cyber-physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyse differences and similarities in behaviour during cyber-, physical-, and cyber-physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"392-406"},"PeriodicalIF":1.7,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139960393","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}

{"title":"Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique","authors":"Dipta Neogi,&nbsp;Mahirul Alam Chowdhury,&nbsp;Mst. Moriom Akter,&nbsp;Md. Ishan Arefin Hossain","doi":"10.1049/cps2.12083","DOIUrl":"10.1049/cps2.12083","url":null,"abstract":"<p>Testing the visual field is a valuable diagnostic tool for identifying eye conditions such as cataract, glaucoma, and retinal disease. Its quick and straightforward testing process has become an essential component in our efforts to prevent blindness. Still, the device must be accessible to the general masses. This research has developed a machine learning model that can work with Edge devices like smartphones. As a result, it is opening the opportunity to integrate the disease-detecting model into multiple Edge devices to automate their operation. The authors intend to use convolutional neural network (CNN) and deep learning to deduce which optimisers have the best results when detecting cataracts from live photos of eyes. This is done by comparing different models and optimisers. Using these methods, a reliable model can be obtained that detects cataracts. The proposed TensorFlow Lite model constructed by combining CNN layers and Adam in this study is called Optimised Light Weight Sequential Deep Learning Model (SDLM). SDLM is trained using a smaller number of CNN layers and parameters, which gives SDLM its compatibility, fast execution time, and low memory requirements. The proposed Android app, I-Scan, uses SDLM in the form of TensorFlow Lite for demonstration of the model in Edge devices.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"269-281"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394799","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":"Real-time implementation for vulnerability of power components under switching attack based on sliding mode","authors":"Seema Yadav,&nbsp;Nand Kishor,&nbsp;Shubhi Purwar,&nbsp;Saikat Chakrabarti,&nbsp;Petra Raussi,&nbsp;Avinash Kumar","doi":"10.1049/cps2.12084","DOIUrl":"https://doi.org/10.1049/cps2.12084","url":null,"abstract":"<p>In recent years, cyber security-related studies in the power grid have drawn wide attention, with much focus on its detection, mainly for data injection type of attacks. The vulnerability of power components as a result of attack and their impact on generator dynamics have been largely ignored so far. With the aim of addressing some of these issues, the authors propose a novel approach using real-time sliding surface-based switching attack (SA) construction. This approach targets the circuit breaker, excitation system, and governor system of the generator. The vulnerability of these power components to cyber-physical attacks and assessment of their potential impact on the stability of generator are discussed. The study is presented to show the progression of cascading generator dynamics on account of single or multiple time instants of SA launched on these power components. The results are discussed according to criteria in terms of deviations in rotor speed of the generator and identify some of possible combinations of power components that are most critical to grid stability. The proposed study is implemented on standard IEEE 3-machine, 9-bus network in real-time digital simulator via transmission control protocol/internet protocol (TCP/IP) communication network established as cyber-physical system. The sliding surface-based SA algorithm developed in MATLAB is launched from another computer.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"375-391"},"PeriodicalIF":1.7,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253747","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":"Learning the geometry of short-circuit faults in power systems for real-time fault detection and classification","authors":"Juan Pablo Naranjo Cuéllar,&nbsp;Gustavo Ramos López,&nbsp;Luis Felipe Giraldo Trujillo","doi":"10.1049/cps2.12074","DOIUrl":"https://doi.org/10.1049/cps2.12074","url":null,"abstract":"<p>Given the short time intervals in which short-circuit faults occur in a power system, a certain time delay between the moment of a fault's inception in the system to the moment in which the fault is actually detected is introduced. In this small time margin, the high amplitudes of the fault current can deal significant damage to the power system. A technique to characterise different types of short circuit faults in a power system for real-time detection, namely AB, BC, CA, ABC, AG, BG and CG faults (and normal operation), is presented based on the geometry of the curve generated by the Clarke transform of the three-phase voltages of the power system. The process was conducted in real time using the <i>HIL402</i> system and a Raspberry Pi 3, and all programming done in the Python programming language. It was concluded that the tested types of faults can be accurately characterised using the eigenvalues and eigenvectors of the matrix that characterises an ellipse associated with each fault: eigenvalues can be used to determine the fault inception distance and eigenvectors can be used to determine the type of fault that occurred. Next, the design of a machine learning model was done based on the previously mentioned characterisation technique. The model was embedded into a Raspberry Pi 3, thus enabling fault detection and classification in a base power system in real time. Finally, the accuracy of the model was tested under different measurement conditions, yielding satisfactory results for a selected set of conditions and overcoming the shortcomings presented in the current research, which do not perform detection and classification in real time.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"8 4","pages":"289-306"},"PeriodicalIF":1.5,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138578065","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: Optimisation, control and AI technology for digital and low-carbon power systems","authors":"Pathmanathan Naidoo,&nbsp;Maria Cristina Tavares,&nbsp;Junwei Cao,&nbsp;Yi Ding,&nbsp;Haochen Hua","doi":"10.1049/cps2.12082","DOIUrl":"https://doi.org/10.1049/cps2.12082","url":null,"abstract":"&lt;p&gt;Modern power systems are facing a growing integration of distributed energy resources (DERs), mainly driven by energy transition, decarbonisation and economic benefits. The deployment of Internet of Things devices transforms the conventional power system into a digitised, cyber, intelligent one, which plays a significant role in grid control and operation and enables numerous smart-grid applications.&lt;/p&gt;&lt;p&gt;The stochastic nature of distributed renewable power generation poses challenges for a power systems operation, while coordinating the dispatch and control of various DERs to reduce operating costs and carbon emissions is essential to improve energy utilisation efficiency. Also, the large-scale connection of DERs increases the complexity of distribution networks, which require more advanced and efficient approaches for system analysis, fault diagnosis and operational optimisation. In this sense, smart monitoring and control systems can also be applied to transmission power networks, enhancing safety and robustness.&lt;/p&gt;&lt;p&gt;Energy internet technology has laid a solid foundation for data-driven analysis, allowing power systems to enter a ‘data-intensive’ era. Currently, huge amounts of data from various sources have been a driving force, enabling big data analytics and artificial intelligence on smart-grid applications, such as planning, operation, energy management, trading, system reliability and resiliency enhancement, system identification and monitoring, fault intelligent perception and diagnosis, and cyber and physical security.&lt;/p&gt;&lt;p&gt;This Special Issue publishes state-of-the-art works related to all aspects of theories and methodologies in optimisation, control and AI technology for digital and low-carbon power systems.&lt;/p&gt;&lt;p&gt;The stochastic nature of distributed renewable generation makes the operation of power systems face the challenge of uncertainty. Thereby, it is of great significance to monitor and identify the real-time state of the new power system. The paper, ‘The real-time state identification of the electricity-heat system based on borderline-SMOTE and XGBoost’ by X. Pei et al., proposes a state identification method based on multi-class data equalisation and extreme gradient boost for systems. The optimal hyperparameters of the model are obtained based on the K-fold cross-validation and grid search.&lt;/p&gt;&lt;p&gt;Reducing carbon emissions is one of the goals of modern power systems operation. Power generation by natural gas, compared with that by coal, has the characteristics of cleanness, efficiency and low carbon. This makes gas-fired power plants popular for undertaking peak regulation tasks in the power systems. The paper, ‘Key problems of gas-fired power plants participating in peak load regulation: a review’ by G. Wang et al., reviews the key problems faced by gas-fired power plants participating in peak load regulation. This paper provides suggestions for the coordinated development of electricity and carbon market in the futur","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"8 4","pages":"219-221"},"PeriodicalIF":1.5,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138578182","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}