[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121990-en":3,"doc-seo-121990-105":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},121990,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Cyber-Securing Medical Devices Using Machine Learning - A Case Study of Pacemaker","This study enhances the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. Key objectives include pinpointing cybersecurity weaknesses, using machine learning to predict security breaches, and proposing countermeasures based on analytical trends. The work contrasts pacemakers’ evolution from basic fixed-rate devices to wireless systems, noting added risks such as unauthorized access, data breaches, and life-threatening malfunctions. A quantitative approach applies the WUSTL-EHMS-2020 dataset and evaluates SVM and GBM with standard metrics.","School of Engineering, Computing and Mathematics Faculty of Science and Engineering  \n2024-10-14  \nCyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker  \nShaymaa Al-Juboori School of Engineering, Computing and Mathematics Suliat Jimoh University of Plymouth  \nLet us know how access to this document benefits you  \nGeneral rights  \nAll content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.  \nTake down policy  \nIf you believe that this document breaches copyright please contact the library providing details, and we will remove access to the work immediately and investigate your claim.  \nFollow this and additional works at: [https://pearl.plymouth.ac.uk/secam-research](https://pearl.plymouth.ac.uk/secam-research)  \nRecommended Citation  \nAl-Juboori, S., & Jimoh, S. (2024) 'Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker', Journal of Informatics and Web Engineering, 3(3), pp. 271-289. Available at:  \n[https://doi.org/10.33093/jiwe.2024.3.3.17](https://doi.org/10.33093/jiwe.2024.3.3.17)  \nThis Article is brought to you for free and open access by the Faculty of Science and Engineering at PEARL. It has been accepted for inclusion in School of Engineering, Computing and Mathematics by an authorized administrator of PEARL. For more information, please [contact openresearch@plymouth.ac.uk](contact openresearch@plymouth.ac.uk).  \nPEARL  \nCyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker  \nAl-Juboori, Shaymaa; Jimoh, Suliat  \nPublished in:  \nJournal of Informatics and Web Engineering  \nDOI:  \n10.33093/jiwe.2024.3.3.17  \nPublication date:  \n2024  \nDocument version:  \nPublisher's PDF, also known as Version of record  \nLink:  \nLink to publication in PEARL  \nCitation for published version (APA):  \nAl-Juboori, S. , & Jimoh, S. (2024) . Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker. Journal of Informatics and Web Engineering , 3(3), 271-289. [https://doi.org/10.33093/jiwe.2024.3.3.17](https://doi.org/10.33093/jiwe.2024.3.3.17)  \nAll content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Wherever possible please cite the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content  \nshould be sought from the publisher or author.  \nDownload date: 28. Oct. 2024  \nJournal 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","cbCaif2qFGr56f4l","https://ap.wps.com/l/cbCaif2qFGr56f4l","pdf",1005514,1,21,"English","en",105,"# Introduction\n# Abstract\n# Keywords\n# Methodology and Machine Learning Approach\n## Data and Features\n## Models and Training\n# Results and Evaluation\n# Conclusion and Future Recommendations","[{\"question\":\"What is the main goal of the study on pacemaker cybersecurity?\",\"answer\":\"The study aims to enhance the pacemaker cybersecurity framework by identifying vulnerabilities and recommending effective strategies.\"},{\"question\":\"How does the study predict cybersecurity threats?\",\"answer\":\"It uses machine learning to predict security breaches by analyzing network traffic features and patients’ biometric features, then evaluating attack labels.\"},{\"question\":\"Which model performs better, GBM or SVM, and what evidence is provided?\",\"answer\":\"Gradient Boosting Machines (GBM) outperform Support Vector Machines (SVM), with higher accuracy (95.1% vs 92.5%), much better recall (94.9% vs 42.7%), and a higher F1 score (76.3% vs 59.0%).\"}]","Cyber-Securing Medical Devices Using Machine Learning - 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