SilentSense: Silent User Identification Via Touch And Movement Behavioral Biometrics

Abstract

In this work, we present SilentSense, a framework to authenticate users silently and transparently by exploiting the user touch behavior biometrics and leveraging the integrated sensors to capture the micro-movement of the device caused by user's screen-touch actions. By tracking the fine-detailed touch actions of the user, we build a "touch-based biometrics" model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, the unique operating pattern of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movement-based biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99%.