Can i borrow your phone?: understanding concerns when sharing mobile phones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors
Proceedings of the fifth ACM conference on Security and Privacy in Wireless and Mobile Networks
Touch me once and i know it's you!: implicit authentication based on touch screen patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tapprints: your finger taps have fingerprints
Proceedings of the 10th international conference on Mobile systems, applications, and services
Distinguishing users with capacitive touch communication
Proceedings of the 18th annual international conference on Mobile computing and networking
Towards application-centric implicit authentication on smartphones
Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
TIPS: context-aware implicit user identification using touch screen in uncontrolled environments
Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
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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%.