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Behavioral biometrics-based continuous authentication is a promisingauthentication scheme, which uses behavioral biometrics recorded by built-insensors to authenticate smartphone users throughout the session. However,current continuous authentication methods suffer some limitations: 1)behavioral biometrics from impostors are needed to train continuousauthentication models. Since the distribution of negative samples from diverseattackers are unknown, it is a difficult problem to solve in real-worldscenarios; 2) most deep learning-based continuous authentication methods needto train two models to improve authentication performance. A deep learningmodel for deep feature extraction, and a machine learning-based classifier forclassification; 3) weak capability of capturing users' behavioral patternsleads to poor authentication performance. To solve these issues, we propose arelative attention-based one-class adversarial autoencoder for continuousauthentication of smartphone users. First, we propose a one-class adversarialautoencoder to learn latent representations of legitimate users' behavioralpatterns, which is trained only with legitimate smartphone users' behavioralbiometrics. Second, we present the relative attention layer to capture richercontextual semantic representation of users' behavioral patterns, whichmodifies the standard self-attention mechanism using convolution projectioninstead of linear projection to perform the attention maps. Experimentalresults demonstrate that we can achieve superior performance of 1.05% EER,1.09% EER, and 1.08% EER with a high authentication frequency (0.7s) on threepublic datasets.

Relative Attention-based One-Class Adversarial Autoencoder for Continuous Authentication of Smartphone Users