A novel dictionary attack on ECG authentication system using adversarial optimization and clustering

Bonan Zhang; Lin Li; Chao Chen; Ickjai Lee; Kyungmi Lee; Tianqing Zhu; Kok-Leong Ong

doi:10.1016/j.knosys.2025.113326

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A novel dictionary attack on ECG authentication system using adversarial optimization and clustering

Journal article

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Peer reviewed

A novel dictionary attack on ECG authentication system using adversarial optimization and clustering

Bonan Zhang, Lin Li, Chao Chen, Ickjai Lee, Kyungmi Lee, Tianqing Zhu and Kok-Leong Ong

Knowledge-based systems, Vol.316(C), pp.1-12

05/2025

DOI: https://doi.org/10.1016/j.knosys.2025.113326

Appears in Recent Faculty of Science and Engineering Publications

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Abstract

Authentication

Biometric

ECG

Security

Electrocardiogram(ECG)-based biometric authentication has become a promising method to improve security in wearable devices due to its inherent uniqueness and difficulty to replicate. However, no studies currently demonstrate that ECG authentication can resist modern attack techniques employed against biometric authentication. In this paper, we present a novel dictionary attack against ECG authentication systems, which poses a significant threat. In contrast to conventional targeted attacks, this approach utilizes random pairing to breach a vast number of users, without requiring specific information about their biometric data. Our approach leverages adversarial optimization and clustering to generate synthetic ECG waveforms capable of bypassing authentication mechanisms of various systems, revealing critical vulnerabilities in the current implementation of ECG-based biometrics. We comprehensively evaluate the effectiveness of this attack across different ECG authentication models, demonstrating that despite the intrinsic uniqueness of ECG signals, a substantial number of users are vulnerable. Our attack method can bypass the authentication system of an average of 20% of users even at the most stringent false acceptance rate of 1%. With up to five attack attempts allowed, our method can bypass up to 62% of users’ ECG authentication models.

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Title: A novel dictionary attack on ECG authentication system using adversarial optimization and clustering
Creators: Bonan Zhang (Author) - RMIT
Lin Li (Author) - Southern Cross University
Chao Chen (Author) - RMIT
Ickjai Lee (Author) - James Cook University
Kyungmi Lee (Author) - James Cook University
Tianqing Zhu (Author) - City University of Macau
Kok-Leong Ong (Author) - RMIT
Publication Details: Knowledge-based systems, Vol.316(C), pp.1-12
Publisher: Elsevier B.V.
Identifiers: 991013285342702368
Academic Unit: Faculty of Science and Engineering
Language: English
Resource Type: Journal article

A novel dictionary attack on ECG authentication system using adversarial optimization and clustering

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