A Machine Learning Attack Resilient and Low-Latency Authentication Scheme for AI-Driven Patient Health Monitoring System

Aug 5, 2026

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Internet of Medical Things (IoMT) and AI models enable wireless Remote Patient Health Monitoring (RPHM), supported by Wi‑Fi and 6G for reliable, low-latency exchange between AI models and IoMT devices. Integrating IoMT with AI and 6G can enable automated diagnostics and personalized care while improving throughput, reliability, low latency, and energy-efficient communication. However, transmitting sensitive medical data over public channels exposes IoMT to security attacks, and mutual authentication and key agreement remain challenging due to privacy and security concerns. Existing schemes often suffer from machine-learning attacks and high latency, motivating a new three-factor ECC-based approach using a one-time PUF (OPUF). The scheme’s security is assessed via informal and formal analysis, and performance is evaluated with multiple metrics to confirm its low-latency and superior results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Introduction

Security Approach and Authentication Scheme

Security Evaluation

Performance Evaluation

Related Work and Index Terms

Please cite the Published Version Ghaffar, Zahid, Kuo, Wen-Chung, Mahmood, Khalid, Tariq, Tayyaba, Bashir, Ali Kashif and Omar, Marwan (2024) A Machine Learning Attack Resilient and Low-Latency Authentication Scheme for AI-Driven Patient Health Monitoring System. IEEE Communications Standards Magazine, 8 (3) . pp. 36-42. ISSN 2471-2825 DOI: [https://doi.org/10.1109/mcomstd.0001.2300055](https://doi.org/10.1109/mcomstd.0001.2300055) Publisher: Institute of Electrical and Electronics Engineers (IEEE) Version: Accepted Version Downloaded from: [https://e-space.mmu.ac.uk/635869/](https://e-space.mmu.ac.uk/635869/) Usage rights: In Copyright Additional Information: 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Enquiries: If you have questions about this document, contact [openresearch@mmu.ac.uk](openresearch@mmu.ac.uk. Please)[. 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 and Low-Latency Authentication Scheme for AI-driven Patient Health Monitoring System Zahid Ghaffar, Wen-Chung Kuo, Khalid Mahmood∗ , Senior Member, IEEE, Tayyaba Tariq, Ali Kashif Bashir, Senior Member, IEEE, Marwan Omar Abstract—The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) models have transformed healthcare by enabling wireless communication for Remote Patient Health Monitoring (RPHM) services. Wireless technologies such as Wi-Fi and 6G support reliable and low-latency communication between AI models and IoMT devices. IoMT devices allow individuals to monitor their health remotely, reducing the need for hospital visits. Integrating IoMT with AI and 6G enables automated diagnostics and personalized care with reduced data transmission among involved entities. It also helps data-intensive applications achieve higher performance levels regarding throughput, reliability, low latency, and energy-efficient communication for AIdriven RPHM system. However, exchanging sensitive information over public channels makes IoMT vulnerable to potential security attacks. Designing effective and secure mutual authentication and key agreement scheme for RPHM has been challenging due to privacy and security concerns. Moreover, there is also a demand for reliable and low-latency communication for AI-driven RPHM systems. Many existing authentication schemes have limitations, including susceptibility to machine learning attacks and high latency rates. To overcome these issues, we present a machinelearning attack-resilient and low-latency authentication scheme for AI-driven RPHM. The proposed scheme utilizes a threefactor approach based on elliptic curve cryptography (ECC). It employs a one-time physical unclonable function (OPUF) to resist machine learning attacks on medical sensing devices. The scheme’s security is evaluated through informal and formal analysis, demonstrating its security strength and persistence. Additionally, the scheme’s performance is assessed using various metrics, confirming its superiority over related schemes and achieving a low latency rate. This work was supported in part by the National Science and Technology Council (NSTC), Taiwan, under Grant NSTC 111-2222-E-224-005- . The authors thank Dr. Khalid Mahmood at the National Yunlin University of Science and Technology for his valuable guidance and suggestions throughou

FAQs

Why is authentication difficult in AI-driven remote patient health monitoring systems?

Sensitive information is exchanged over public channels, making IoMT vulnerable to security attacks. Privacy and security requirements also make mutual authentication and key agreement challenging.

What main cryptographic design does the proposed scheme use?

The scheme uses a three-factor approach based on elliptic curve cryptography (ECC) and employs a one-time physical unclonable function (OPUF) to resist machine learning attacks.

How are security and performance of the scheme evaluated?

Security is evaluated through both informal and formal analysis to demonstrate strength and persistence. Performance is assessed using multiple metrics, showing a low-latency rate and superiority over related schemes.

Aug 5, 2026

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