An investigation of vulnerabilities in Internet of Medical Things (IoMT) and development of a benchmark dataset using smartwatch
Abstract
The rapid integration of Internet of Medical Things (IoMT) devices into healthcare has significantly enhanced patient care, but it has also introduced numerous security vulnerabilities. This paper investigates these vulnerabilities and presents a benchmark dataset developed to reflect Bluetooth based cyberattacks targeting IoMT devices that machine learning result was presented. Our research aims to enhance the detection and prevention capabilities in the cybersecurity domain. We address the lack of comprehensive datasets representing cyberattacks on IoMT devices and smartphone together, necessary for utilizing machine learning algorithms to detect and prevent such vulnerabilities.
Keywords
IoT, internet of medical things, cybersecurity, bluetooth attacks, machine learning, healthcare security
Document Type
Book Chapter
Date of Publication
1-1-2025
Publication Title
Cybersecurity for Internet of Health Things
Publisher
Taylor & Francis
School
School of Science
Copyright
subscription content
First Page
67
Last Page
78
Comments
Look, K. H., & Ritz, W. K. F. (2025). An investigation of vulnerabilities in Internet of Medical Things (IoMT) and development of a benchmark dataset using smartwatch. In Cybersecurity for Internet of Health Things (pp. 56–78). Taylor & Francis. https://doi.org/10.1201/9781003483267-5