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Recently, mobile technology has become closely linked with our daily activities. Smartphones are used for multiple personal tasks involving private information, such as communication, healthcare, and banking. Therefore, there is a high demand for user-friendly authentication methods that prevent unauthorized access to sensitive information. This paper proposes a novel feature representation tactic for continuous authentication named Multiple Channels Biological Graph (MCBG). Unlike conventional techniques, MCBG divides the smartphone usage scenarios into more fine-grained cases, including the operation interval features. To this end, we extract the screen touch and handheld features from multiple built-in sensors without extra user interaction. We conduct experiments on 180 participants (130 adults and 50 minors) and investigate the sufficiency of different sensor combinations required to authenticate identity accurately. Results show that our MCBG-based model achieves 99.38% authentication accuracy within 1.9 seconds. Furthermore, MCBG also represents the intrinsic differences between grown-ups and minors, achieving 96% identification accuracy.

Who Is Using the Phone? Representation-Learning-Based Continuous Authentication on Smartphones