Designing touch-based hybrid authentication method for smartphones

With the privilege of using mobile devices it is crucial to protect Smartphones by authenticating legitimate users, while blocking attacker's access. Biometric authentication consists of physiological and behavioural authentication. Behavioural authentication system on smartphones is based on creating a regular behavioural model using adaptive machine learning classifiers. This paper aims to establish a normal-behavioural model and comparing it with the existing established model. This paper also proposes a hybrid authentication scheme comprises of continuous authentication (CA) and implicit authentication (IA) based on touch gestures. Particularly, the 14 gestures were extracted from touch-based gesture data that was collected from users' interaction with Android smartphones. The first evaluation results on a set of dataset prove that a neural network classifier is better fit to authenticate different users. Next, the Practical Swarm Optimisation (PSO) - Radial Basis Function Network (RBFN) classifier was used on the same datasets, which produced better results. Finally, users' data collected (actual dataset) was used to train and test all 6 classifiers including PSO-RBFN. The result of PSO-RBFN is the average error rate of 1.9%, which is encouraging. Moreover, combining the proposed CA scheme with an IA scheme, which is a pattern based will dramatically reduce the error rate to nearly 0.