DeepAuth
Sara Amini, Vahid Noroozi, Amit Pande, Satyajit Gupte, Philip S. Yu, Chris Kanich · 2018
With the increasing volume of transactions taking place online, mobile fraud has also increased. Mobile applications often authenticate the user only at install time. The user may then remain logged in for hours or weeks. Any unauthorized access may lead to financial, criminal or privacy losses. In this work, we leverage currently available built-in motion sensors in smartphones to learn users' behavioral characteristics while interacting with the mobile device to provide an implicit re-authentication mechanism that enables a frictionless and secure user experience in the application. This approach improves the generality as well as power efficiency of the authentication mechanism compared to using the camera feed which involves (a) specific hardware, (b) higher battery usage and (c) privacy concerns. We present DeepAuth as a generic framework for re-authenticating users in a mobile app. In our approach, we use time and frequency domain features extracted from motion sensors and a long short-term memory (LSTM) model with negative sampling to build a re-authentication framework. The framework is able to re-authenticate a user with 96.70% accuracy in 20 seconds from a set of data collected from 47 volunteers.