Cyber-Securing Medical Devices Using Machine Learning - A Case Study of Pacemaker

Aug 4, 2026

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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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Abstract

Keywords

Methodology and Machine Learning Approach

Data and Features

Models and Training

Results and Evaluation

Conclusion and Future Recommendations

School of Engineering, Computing and Mathematics Faculty of Science and Engineering 2024-10-14 Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker Shaymaa Al-Juboori School of Engineering, Computing and Mathematics Suliat Jimoh University of Plymouth Let us know how access to this document benefits you General rights All 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. Take down policy If 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. Follow this and additional works at: [https://pearl.plymouth.ac.uk/secam-research](https://pearl.plymouth.ac.uk/secam-research) Recommended Citation Al-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: [https://doi.org/10.33093/jiwe.2024.3.3.17](https://doi.org/10.33093/jiwe.2024.3.3.17) This 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). PEARL Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker Al-Juboori, Shaymaa; Jimoh, Suliat Published in: Journal of Informatics and Web Engineering DOI: 10.33093/jiwe.2024.3.3.17 Publication date: 2024 Document version: Publisher's PDF, also known as Version of record Link: Link to publication in PEARL Citation for published version (APA): Al-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) All 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 should be sought from the publisher or author. Download date: 28. Oct. 2024 Journal of Informatics and Web Engineering Vol. 3 No. 3 (October 2024) eISSN: 2821-370X Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker Suliat Toyosi Jimoh1, Shaymaa S Al-juboori2* 1,2School of Computing, Engineering and Mathematics, University of Plymouth, Drake Circus, Plymouth PL4 8AA, United Kingdom *Corresponding author: ([shaymaa.al-juboori@plymouth.ac.uk](shaymaa.al-juboori@plymouth.ac.uk); ORCiD: 0000-0001-5175-736X) Abstract-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

FAQs

What is the main goal of the study on pacemaker cybersecurity?

The study aims to enhance the pacemaker cybersecurity framework by identifying vulnerabilities and recommending effective strategies.

How does the study predict cybersecurity threats?

It uses machine learning to predict security breaches by analyzing network traffic features and patients’ biometric features, then evaluating attack labels.

Which model performs better, GBM or SVM, and what evidence is provided?

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%).

Aug 4, 2026

Upload by Maeve