Attacking Mouse Dynamics Authentication using Novel Wasserstein Conditional DCGAN

Behavioral biometrics is an emerging trend due to their cost-effectiveness and non-intrusive implementations that support remote access for user identification. This is the case especially in recent times of social distancing and working from home arrangements, where online attendance is the preferred option in contrast to physical presence. In this work, we explore the limitations of mouse dynamics authentication by impersonating legitimate user mouse action sequences. Specifically, towards that aim, we develop a novel generative WC-DCGAN model to generate highly accurate fake user action sequences. We apply our WC-DCGAN to this problem and show that it causes the target classifier can be tricked into identifying a fraudster as a legitimate user. WC-DCGAN has several benefits, including: achieving dominated convergence, hence implying the existence of solutions and optimal discriminator regardless of data and generator distributions; and acting as an unsupervised model for a fixed class label and generator. Experiments are conducted to verify these points. Subsequently, we analyzed the cause of misclassifications, and propose a novel mouse dynamics strategy that offers much tighter authentication with significant reductions in misclassification events.