Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial login mechanism, it is highly desirable to re-authenticate endusers who are continuing to access security-critical services and data, whether in the cloud or in the smartphone. But attackers who have gained access to a logged-in smartphone have no incentive to re-authenticate, so this must be done in an automatic, non-bypassable way. Hence, this paper proposes a novel authentication system, iAuth, for implicit, continuous authentication of the end-user based on his or her behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We design a system that gives accurate authentication using machine learning and sensor data from multiple mobile devices. Our system can achieve 92.1% authentication accuracy with negligible system overhead and less than 2% battery consumption.
Lee and Lee REF proposed a legitimate user identification system named iAuth for implicit but continuous user identification in which the end user is identified based on their behavioral characteristics by leveraging the built-in sensors.
Implicit Sensor-based Authentication of Smartphone Users with Smartwatch
HASP 2016
Computer Science