A User Security Continuous Authentication Method for Earable Devices

Wang Yong; Xiong Yi; Yang Tianyu; Shen Yiran

doi:10.7544/issn1000-1239.202440415

Journal of Computer Research and Development > 2024 > 61(11): 2821-2834. > DOI: 10.7544/issn1000-1239.202440415 CSTR: 32373.14.issn1000-1239.202440415

Wang Yong, Xiong Yi, Yang Tianyu, Shen Yiran. A User Security Continuous Authentication Method for Earable Devices[J]. Journal of Computer Research and Development, 2024, 61(11): 2821-2834. DOI: 10.7544/issn1000-1239.202440415

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A User Security Continuous Authentication Method for Earable Devices

Wang Yong^{1, 2,},
Xiong Yi¹,
Yang Tianyu^1, ,,
Shen Yiran³

1.
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001
2.
Modeling and Emulation in E-Government National Engineering Laboratory (Harbin Engineering University), Harbin 150001
3.
School of Software, Shandong University, Jinan 250100

Funds: This work was supported by the National Natural Science Foundation of China (61672179), the Youth Fund Project of Humanities and Social Sciences Research of the Ministry of Education of China (20YJCZH172), the China Postdoctoral Science Foundation (2019M651262), and the Funding from the Basic Research Support Program for Outstanding Young Teachers in Provincial Undergraduate Universities in Heilongjiang Province（YQJH2023302）.

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Author Bio:
Wang Yong: born in 1983. PhD, associate professor. Senior member of CCF. His main research interests include artificial intelligence, privacy computing, and Internet of things

Xiong Yi: born in 2003. Bachelor. His main research interests include smart wearable devices, intelligent Internet of things, and machine learning

Yang Tianyu: born in 2002. Bachelor. His research interests include smart wearable devices, intelligent Internet of things, and machine learning

Shen Yiran: born in 1987. PhD, professor. Member of CCF， senior member of IEEE. His main research interests include mobile computing and virtual reality
Received Date: May 30, 2024
Revised Date: September 05, 2024
Available Online: September 12, 2024

Graphical Abstract

Abstract

Abstract

Earable devices are used as typical AIoT edge sensing devices. Protecting the privacy of legitimate users and preventing illegal use has become extremely important. In response to the current user authentication methods for earable devices, which are limited by input interfaces, sensor costs, and device power consumption, resulting in insufficient security, low universality, and poor user experience, a user authentication model based on the built-in inertial measurement unit (IMU) of earable devices is proposed. This model extracts user-specific information by collecting vibration signals generated by users performing facial interaction gestures, and achieves diversified implicit continuous user authentication based on intelligent analysis of the above information. To extract accurate and reliable user-specific information, a deep neural network feature encoder based on a Siamese network is proposed, which maps gesture samples of the same user closer in the feature space and enlarges the distance between gesture samples of different users, achieving effective encoding of user-specific information. For continuous user authentication based on user-specific information, a weighted voting strategy based on the distance of the one-class support vector machine hyperplane is proposed, which can adaptively optimize the discrimination boundary to better capture the contained features and structures. The confidence level of the sample is determined based on the distance of the sample points inside and outside the hyperplane, and a weighted voting is designed for authentication. Experimental results show that the method in this paper achieves an authentication accuracy of 97.33% in a single vote, and achieves an authentication accuracy of 99.993% after seven rounds of continuous authentication, which is better than all the methods compared in this paper. It provides a smoother user experience and a higher level of security without the need for passwords, and has a high practical application value.
- AIoT,
- earable devices,
- user continuous authentication,
- deep neural network feature encoder,
- weighted voting strategy

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References (36)

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