SilentSense: Silent User Identification via Dynamics of Touch and Movement Behavioral Biometrics

TL;DR

SilentSense is a framework that uses touch and micro-movement biometrics to authenticate smartphone users silently and accurately, achieving over 99% identification accuracy in real-world tests.

Contribution

It introduces a novel silent user authentication method combining touch and micro-movement biometrics, effective even during user mobility.

Findings

User identification accuracy exceeds 99%

Effective in silent and transparent authentication

Robust during user mobility

Abstract

With the increased popularity of smartphones, various security threats and privacy leakages targeting them are discovered and investigated. In this work, we present \ourprotocoltight, a framework to authenticate users silently and transparently by exploiting dynamics mined from the user touch behavior biometrics and the micro-movement of the device caused by user's screen-touch actions. 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 dynamics 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%.