Continuous Authentication Using Smartphone Inertial Sensors and Deep Learning

• Ismael J. V. Paz UFAM http://orcid.org/0000-0003-0568-8150
• Hendrio L. S. Bragança UFAM https://orcid.org/0000-0003-1479-1707
• Eduardo Souto UFAM https://orcid.org/0000-0003-0003-908X

DOI: https://doi.org/10.5753/sbseg.2022.225312

Abstract

Smartphones are part of our daily lives and can help perform various tasks, from measuring physical activities to banking operations. To ensure the data security of this device, most systems employ static authentication solutions, such as password, pattern, PIN or fingerprint. However, in a scenario where an imposter user has access to the passwords or gains physical access to the unlocked device, all sensitive data ends up being exposed. To deal with this problem, this work proposes a continuous authentication method for mobile devices using data from inertial sensors. The process of identifying the genuine or imposter user is performed through an authentication model defined from a deep network architecture based on convolutional neural networks with recurrent layers. Furthermore, this work employs a trust model to avoid blocking genuine users and prevent an imposter from being undetected for a long time. Tests using data from 30 users show that the proposed model can detect imposter users in up to 61 seconds.

Keywords: Continuous authentication, Inertial Sensors, Deep Neural Networks, DeepConvLSTM Networks

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