Using Feature Fusion Strategies in Continuous Authentication on Smartphones

Abstract

With the increasing prevalence of mobile devices, people prefer to use smartphones to make payments, take photos, and collect personal vital information. Due to the high possibility of smartphone illegal access, the security and privacy of the devices become more important and critical. In this article, we present FusionAuth, a sensor-based continuous authentication system leveraging the accelerometer, gyroscope, and magnetometer on smartphones to capture users’ behavioral patterns. In order to improve the authentication performance and enhance system reliability, we are among the first to utilize two feature fusion strategies of serial feature fusion and parallel feature fusion to combine the designed features from the three sensors in the feature extraction module. Based on the trained one-class support vector domain description classifier, we evaluate the authentication performance of FusionAuth in terms of impact of window size and user size, and accuracy on different users. The experimental results demonstrate that FusionAuth reaches 1.47% mean balanced error rate with the serial fusion and achieves 1.79% mean BER with the parallel fusion.