Performance Analysis of Touch-Interaction Behavior for Active Smartphone Authentication

The increasing use of touchscreen smartphones to access sensitive and privacy data has given rise to the need of secure and usable authentication technique. Smartphone users have their own unique behavioral characteristics when performing touch operations. These personal characteristics are reflected on different rhythm, strength, and angle preferences of touch-interaction behavior. This paper investigates the reliability and applicability on the usage of users' touch-interaction behavior for active authentication on smartphones. For each common type of touch operations, both static and dynamic features are extracted and analyzed for fine-grained characterization of users' touch behavior. Classification techniques (nearest neighbor, neural network, support vector machine, and random forest) are applied to the feature space for performing the task of active authentication. Analyses are conducted using data from around 134 900 touch operations of 71 participants in real-world scenarios, and the authentication performance is evaluated across various types of touch operations, varying operation lengths, different application tasks, and different application scenarios. The extensive experimental results are included to show that touch-interaction behavior exhibits sufficient discriminability and stability among smartphone users for active authentication, and achieves equal-error rates between 1.72% and 9.01% for different types of touch operations with the operation length of 11; the authentication accuracies improve when having long observation or small timespan between the training and testing phases, and express more reliably and stably in a specific task than in the free task. We also discuss a number of avenues for additional research that we believe are necessary to advance the state-of-the-art in this area.