TBAuth: A continuous authentication framework based on tap behavior for smartphones

With the widespread adoption of smartphones, the security risks associated by the single authentication scheme have become increasingly serious. This promptes researchers to shift focus towards continuous authentication(CA) techniques. tap is a kind of behavior that can reflect the user’s identity. In this paper, a CA framework based on tap behavior (TBAuth) is proposed, which utilizes a vibration model to model tap behavior. In order to fully explore the potential of tap behavior, we analyze the user identity information embedded in it and its correlation with vibration. The data from motion sensors and touchscreen sensors are combined to fully extract tap behavioral features, and a feature selection method is proposed. In addition, a single classifier local outlier factor (LOF) is used to train authentication model. We validate TBAuth using a dataset of tap behavior collected from 40 volunteers. Extensive experiments are conducted, including method evaluation, authentication performance evaluation, and simulation experiments. The experimental results demonstrate that excellent user identity verification performance is achieved by TBAuth, as it can achieve an authentication performance as low as a 2.95% equal error rate (EER).