Wenqing Yang
ABSTRACT
Continuous Implicit Authentication (CIA) provides a imperceptible and passive validation in a whole process of operating the mobile devices. In this paper, we describe users’ behavioral patterns through the build-in sensors of smartphones, and apply them to our authentication system. The current work is mainly implemented in a controlled experimental environment. We prove the effectiveness of the implicit authentication method in the natural environment through the analysis of only the touch screen process and the entire process of the user using the mobile phone. We focus on the multi-class strategy in CIA, in which a classifier has to recognize the “owner” of a series of operations from identity banks. To extract temporal domain and space domain features and enhance the classifier's discriminative power, our system utilizes Resnet with Squeeze-and-Excitation incorporating Attention blocks and Additive Angular Margin Loss. We evaluate our system both in our dataset and BrainRun dataset, and the accuracies of distinguish the correct identity achieves are 96.94% and 87.11% respectively. Specially, with the continuous data in BrainRun, the result is generated from behaviors during 5 s in a more interactive game.
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