Mobile gesture-based iPhone user authentication

Abstract

Efforts have been made to introduce an extra layer of security on mobile devices, including a good amount of research initiated in the behavioral biometrics domain. However, all prior research approaches for mobile gesture-based authentication has been carried out uni-directionally. Despite of the fact that there are many devices with their own configurations, the study of mobile authentication based on behavioral biometrics has been done only with the Android operating system and devices. In this paper, a novel approach to identifying the owner of a mobile device based on Behavioral Biometrics Mobile Gestures Recognition is presented. This research takes the first step towards implementing behavioral biometrics identification for iOS based iPhone devices. In this research work, it is shown that a user can be identified as the true owner or an imposter of such a device based on the interactive behavior and gestures of the user. In this way continuous identification or authentication of an owner can be done based on the interaction of the user and the device. It is shown that a continuous authentication mechanism can be established using a self-learning model based on machine learning classification approaches such as Random Forests, Gradient Boosting Machine, Deep Learning, and Naive Bayes. The results in this paper show that, with behavioral biometrics, automated user authentication mechanism, EER (Equal Error Rate) can be improved to around 27%, clearly demonstrating that the chances of authenticating the user are good.