An HMM-based behavior modeling approach for continuous mobile authentication

TL;DR

This paper introduces a novel HMM-based continuous authentication method for mobile devices that models user touch behaviors without requiring data from other users, demonstrating superior performance over existing techniques.

Contribution

It presents a new HMM-based approach for behavioral modeling in mobile authentication that does not need cross-user training data.

Findings

The proposed method outperforms state-of-the-art techniques.

Modeling horizontal and vertical scrolling improves accuracy.

Extensive experiments validate the effectiveness of the approach.

Abstract

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.