Standardizing the evaluation framework for ECG-based authentication in IoT devices

Zhang, Bonan, Li, Lin, Chen, Chao, Lee, Ickjai, Lee, Kyungmi, and Ong, Kok Leong (2025) Standardizing the evaluation framework for ECG-based authentication in IoT devices. Computer Communications, 240. 108201.

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Abstract

Devices on the Internet of Things (IoT) often have constrained resources and operate in diverse environments, making them vulnerable to unauthorized access and cyber threats. Electrocardiogram (ECG) signals have emerged as a promising biometric for authenticating users in such settings. However, current ECG-based authentication studies lack a standardized evaluation framework tailored to resource-limited IoT contexts and long-term usage, making it difficult to assess their practical reliability. In this paper, we introduce a new evaluation framework for ECG-based authentication on IoT devices and construct a standardized dataset to facilitate rigorous testing. We categorize performance metrics into four key dimensions: scalability, adaptability, efficiency, and cancelability. Using this framework, we evaluate four representative ECG authentication algorithms for IoT devices. The results show that these algorithms struggle to maintain consistent performance under cross-session authentication scenarios. These findings highlight the critical importance of addressing the temporal variability of ECG signals and the current gap in robust ECG-based authentication for IoT devices. We believe the proposed framework will guide future research toward more resilient and secure ECG authentication systems for the IoT.

Item ID: 87856
Item Type: Article (Research - C1)
ISSN: 1873-703X
Keywords: AI, Biometric, ECG authentication, IoT
Copyright Information: © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited: 03 Mar 2026 00:29
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3201 Cardiovascular medicine and haematology > 320101 Cardiology (incl. cardiovascular diseases) @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health @ 50%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100%
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