This paper studies continuous authentication for touch interface based mobile devices. A Hidden Markov Model (HMM) based behavioral template training approach is presented, which does not require training data from other subjects other than the owner of the mobile. The stroke patterns of a user are modeled using a continuous left-right HMM. The approach models the horizontal and vertical scrolling patterns of a user since these are the basic and mostly used interactions on a mobile device. The effectiveness of the proposed method is evaluated through extensive experiments using the Toucha-lytics database which comprises of touch data over time. The results show that the performance of the proposed approach is better than the state-of-the-art method.
Cite
@article{arxiv.1712.08307,
title = {An HMM-based behavior modeling approach for continuous mobile authentication},
author = {Aditi Roy and Tzipora Halevi and Nasir Memon},
journal= {arXiv preprint arXiv:1712.08307},
year = {2017}
}