A Machine Learning Attack Resilient Authentication Protocol for AI-Driven Consumer Wearable Health Monitoring - Research paper overview

Aug 3, 2026

The Internet of Medical Things (IoMT) supports remote patient health monitoring by connecting consumer medical devices and wearable sensors, enabling AI-driven analytics for timely diagnostics and more personalized care. Wireless links between wearable devices, however, create security and privacy vulnerabilities, including machine learning-based attacks, physical tampering, and impersonation. To address insufficient resilience in existing authentication approaches, the protocol integrates an OPUF, providing mutual authentication and anonymity while resisting common threats. Formal and informal analyses plus performance evaluation show reduced communication and computation costs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Background on IoMT and AI-driven RPHM

Security and privacy challenges in wearable communications

Proposed protocol

OPUF integration for ML-attack resilience

Security analysis

Mutual authentication and anonymity

Performance evaluation

Communication and computation cost comparison

Please cite the Published Version Ghaffar, Zahid , Kuo, Wen-Chung , Mahmood, Khalid , Alturki, Nazik , Saleem, Muhammad Assad and Bashir, Ali Kashif (2025) A Machine Learning Attack Resilient Authentication Protocol for AI-Driven Consumer Wearable Health Monitoring. IEEE Transactions on Consumer Electronics. ISSN 0098-3063 DOI: [https://doi.org/10.1109/tce.2025.3593648](https://doi.org/10.1109/tce.2025.3593648) Publisher: Institute of Electrical and Electronics Engineers (IEEE) Version: Accepted Version Downloaded from: [https://e-space.mmu.ac.uk/641814/](https://e-space.mmu.ac.uk/641814/) Usage rights: Creative Commons: Attribution 4 .0 Additional Information: This is an author accepted manuscript of an article published in IEEE Transactions on Consumer Electronics. This version is deposited with a Creative Commons Attribution 4.0 licence [ [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]. The version of record can be found on the publisher’s website. Enquiries: If you have questions about this document, contact [openresearch@mmu.ac.uk. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party’s rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines)) A Machine Learning Attack Resilient Authentication Protocol for AI-Driven Consumer Wearable Health Monitoring Zahid Ghaffar, Wen-Chung Kuo, Khalid Mahmood Senior Member, IEEE, Nazik Alturki, Muhammad Asad Saleem, Ali Kashif Bashir Senior Member, IEEE Abstract—The Internet of Medical Things (IoMT) is transforming healthcare by integrating interconnected consumer medical devices and sensors for remote patient health monitoring (RPHM). Integrating IoMT with Artificial Intelligence (AI) enables automated diagnostics and personalized healthcare while optimizing reliability and efficiency. It transforms healthcare by enabling RPHM through interconnected medical devices, wearable sensors, consumer health devices, and healthcare infrastructure. However, wireless communication among consumer wearable devices introduces significant security and privacy concerns, making them vulnerable to machine learning-based attacks, physical tampering, and impersonation threats. Although there are several authentication protocols, many do not provide robust resilience against these emerging threats. Therefore, we propose a machine learning attack resilient authentication protocol for AI-driven consumer wearable health monitoring to address these challenges. The protocol integrates an OPUF to mitigate machine learning-based attacks. We perform formal and informal security analyses, demonstrating that the proposed protocol provides mutual authentication, anonymity, and resistance to common security threats. Furthermore, the performance evaluation shows that the protocol significantly reduces communication and computation costs compared to existing protocols. Index Terms—Authentication and Key Agreement, Authentication protocol, Remote Patient Health Monitoring I. INTRODUCTION The Internet of Medical Things (IoMT) comprises a network of smart medical devices and sensors, enabling seamless data This work is supported by Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia through the Researchers Supporting Project PNURSP2025R333 . Zahid Ghaffar is with the Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu 64002, Taiwan. (e-mail: [chzahid337@gmail.com](chzahid337@gmail.com)) Wen-Chung Kuo is with the Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu 64002, Yunlin, Taiwan (e-mail: [simonkuo@yuntech.edu.tw](simonkuo@yuntech.edu

FAQs

What evidence is provided to validate the protocol’s effectiveness?

The document reports formal and informal security analyses demonstrating resilience against common threats, and performance evaluation showing significant reductions in communication and computation costs compared with existing protocols.

Aug 3, 2026

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