Enhancing Sensor-Based Mobile User Authentication in a Complex Environment by Deep Learning

Zhengqiu Weng, Shuying Wu (), Qiang Wang and Tiantian Zhu
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Zhengqiu Weng: School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325035, China
Shuying Wu: School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325035, China
Qiang Wang: School of Economics and Management, Wenzhou University of Technology, Wenzhou 325035, China
Tiantian Zhu: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China

Mathematics, 2023, vol. 11, issue 17, 1-15

Abstract: With the advent of smart mobile devices, end users get used to transmitting and storing their individual privacy in them, which, however, has aroused prominent security concerns inevitably. In recent years, numerous researchers have primarily proposed to utilize motion sensors to explore implicit authentication techniques. Nonetheless, for them, there are some significant challenges in real-world scenarios. For example, depending on the expert knowledge, the authentication accuracy is relatively low due to some difficulties in extracting user micro features, and noisy labels in the training phrase. To this end, this paper presents a real-time sensor-based mobile user authentication approach, ST-SVD, a semi-supervised Teacher–Student (TS) tri-training algorithm, and a system with client–server (C-S) architecture. (1) With S-transform and singular value decomposition (ST-SVD), we enhance user micro features by transforming time-series signals into 2D time-frequency images. (2) We employ a Teacher–Student Tri-Training algorithm to reduce label noise within the training sets. (3) To obtain a set of robust parameters for user authentication, we input the well-labeled samples into a CNN (convolutional neural network) model, which validates our proposed system. Experimental results on large-scale datasets show that our approach achieves authentication accuracy of 96.32%, higher than the existing state-of-the-art methods.

Keywords: mobile user authentication; deep learning; large-scale data analysis; implicit authentication; user micro feature (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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