Authenticating Phone Users Using a Gait-Based Histogram Approach on Mobile App Sessions

Collectively, user-friendly interfaces, small but impactful sensing technologies, intuitive device designs, and the variety of mobile applications (or apps) have transformed the expectations for cellular phones. Apps are a primary factor in device functionality; they allow users to quickly carry out tasks directly on their device. This paper leverages mobile apps for continuous authentication of mobile device users. We borrow from a gait-based approach by continuously extracting n-bin histograms from numerically encoded app data. Since more active subjects will generate more data, it would be trivial to distinguish between these subjects and others which are not as active. Thus, we divided a dataset of 19 months of app data from 181 subjects into three datasets to determine if minimally active, moderately active, or very active subjects were more challenging to authenticate. Using the absolute distance between two histograms, our approach yielded a worst-case EER of 0.188 and a best-case EER of 0.036 with a worst-case initial training period of 1.06 hours. We also show a positive correlation between user activity level and performance, and template size and performance. Our method is characterized by minimal training samples and a context-independent evaluation, addressing important factors which are known to affect the practicality of continuous authentication systems.