A framework of dynamic selection method for user classification in touch-based continuous mobile device authentication

Abstract

Continuous authentication can provide a mechanism to continuously monitor mobile devices while a user is actively using it, after passing the initial-login authentication phase. Touch biometric is one of the promising modality to realise continuous authentication on mobile devices by distinguishing between the touch strokes performed by the legitimate and illegitimate users through classification algorithms. While the benefit of the scheme is promising, the effectiveness of different classification methods are not thoroughly understood. Little consideration has been given on the combination of multiple classifiers to perform continuous authentication. In this paper, we propose a novel classification framework for touch-based continuous mobile device authentication (CMDA), utilising dynamic selection of classifiers (DS). Instead of classifying all touch strokes using the same classifier, the proposed framework classifies each touch sample using the most promising classifier(s) from a pool of classifiers. Based on the proposed framework, we evaluated various DS methods in multiple scenarios across four touch datasets. The aim of this evaluation is to assess the feasibility of DS on touch-based CMDA. We then compared these DS methods with well-known single classifiers and static ensemble methods. The experimental results show the potential and feasibility of the DS methods to improve the authentication performance of touch-based CMDA against the benchmark methods. We found that DS methods are capable of producing promising results with relatively low equal error rate (EER) in many scenarios of the datasets, with relatively high consistencies. The obtained results would be valuable for further enhancement of existing user classification methods and the development of new DS methods in touch-based CMDA.