[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122582-en":3,"doc-seo-122582-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122582,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Cyber-Securing Medical Devices Using Machine Learning - A Case Study of Pacemaker","This study strengthens the cybersecurity framework of pacemaker devices by systematically identifying vulnerabilities and proposing actionable defenses. Research objectives include locating cybersecurity weaknesses, applying machine learning to predict security breaches, and deriving countermeasures from analytical trends. The work reviews pacemaker evolution from fixed-rate devices to wireless systems, which improves care while expanding risk. It uses the WUSTL-EHMS-2020 dataset with network traffic features, biometric features, and attack labels, then evaluates models via accuracy, precision, recall, and F1.","Journal of Informatics and Web Engineering  \nVol. 3 No. 3 (October 2024) eISSN: 2821-370X  \nCyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker  \nSuliat Toyosi Jimoh1, Shaymaa S Al-juboori2*  \n1,2School of Computing, Engineering and Mathematics, University of Plymouth, Drake Circus, Plymouth PL4 8AA, United Kingdom  \n*Corresponding author: ([shaymaa.al-juboori@plymouth.ac.uk](shaymaa.al-juboori@plymouth.ac.uk); ORCiD: 0000-0001-5175-736X)  \nAbstract-This study aims to enhance the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. The objectives are to pinpoint cybersecurity weaknesses, utilize machine learning to predict security breaches, and propose countermeasures based on analytical trends. The literature review highlights the transformation of pacemaker technology from basic, fixed-rate devices to sophisticated systems with wireless capabilities, which, while improving patient care, also introduce significant cybersecurity risks. These risks include unauthorized entry, data breaches, and lifethreatening device malfunctions. The methodology in this study utilizes a quantitative research approach using the WUSTL-EHMS- 2020 dataset, which includes network traffic features, patients' biometric features, and attack label. The step-by-step method of machine learning prediction includes data collection, data preprocessing, feature engineering, and models’ training using Support Vector Machines (SVM) and Gradient Boosting Machines (GBM) . The implementation results used evaluation metrics like accuracy, precision, recall, and F1 score to show that GBM model outperformed the SVM model. The GBM model achieved higher accuracy of 95.1% compared to 92.5% for SVM, greater precision of 99.6% compared to 96.7% for SVM, better recall of 94.9% compared to 42.7% for SVM, and a higher F1 score of 76.3% compared to 59.0% for SVM, making GBM model more effective in predicting cybersecurity threats. This study concludes that GBM is an effective machine learning model for enhancing pacemaker cybersecurity by analyzing network traffic and biometric data patterns. Future recommendations for improving the pacemaker cybersecurity include implementing GBM model for threat predictions, integration with existing security measures, and regular model updates and retraining.  \nKeywords—Cybersecurity, Pacemaker, Vulnerabilities, Machine Learning, Threat Prediction.  \nReceived: 10 July 2024; Accepted: 30 August 2024; Published: 16 October 2024  \nThis is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nThe rapid development of technology has transformed how medical procedures are performed, especially through the integration of advanced electronic devices [1], [2], [3] . As mentioned in [4], a significant development in modern medicine is the widespread use of medical implantable devices like pacemakers. These pacemakers, which are designed to regulate heart rhythms, have improved patients’ lives and overall wellbeing. The essential features that make pacemakers revolutionary are their ability to communicate wirelessly with healthcare providers, and adjust to patients needs through remote monitoring, which also introduces a variety of vulnerabilities. The global landscape of  \nthreats has undergone significant transformation alongside the rapid evolution of a digitally interconnected society. Medical devices, once seen purely in the light of their therapeutic potentials, are now viewed through the lens of cybersecurity [6] . With the advent of IoT (Internet of Things), home appliances and medical equipment are now interconnected in ways that were not previously adopted, enabling streamlined operations and remote access functionalities [1] . However, this interconnectivity has increased the attack surface for potential adversaries [5], [7]  \n[8] .  \nThe above-mentioned points have evidenced the importance of ensuring pacemaker cybersecurit","cbCailo7DATbP3ro","https://ap.wps.com/l/cbCailo7DATbP3ro","pdf",869369,1,19,"English","en",105,"# Introduction\n## Background and motivation\n## Cyber risks and societal implications\n## Study aim","[{\"question\":\"What is the main goal of the study on pacemaker cybersecurity?\",\"answer\":\"The study aims to enhance pacemaker device cybersecurity by identifying vulnerabilities and recommending effective strategies.\"},{\"question\":\"Which dataset and features are used for machine learning prediction?\",\"answer\":\"The study uses the WUSTL-EHMS-2020 dataset with network traffic features, patients’ biometric features, and attack labels.\"},{\"question\":\"Why does the study conclude that GBM performs better than SVM?\",\"answer\":\"Evaluation metrics show GBM outperforms SVM, including higher accuracy (95.1% vs 92.5%), precision (99.6% vs 96.7%), recall (94.9% vs 42.7%), and F1 score (76.3% vs 59.0%).\"}]","Cyber-Securing Medical Devices Using Machine Learning - 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