Using Data Augmentation in Continuous Authentication on Smartphones

As personal computing platforms, smartphones are commonly used to store private, sensitive, and security information, such as photographs, emails, and Android Pay. To protect such information from adversaries, continuous authentication on smartphone users becomes more and more important. In this paper, we present a novel authentication system, SensorAuth, for continuous authentication of users based on their behavioral patterns, by leveraging the accelerometer and gyroscope ubiquitously built into smartphones. We are among the first to exploit five data augmentation approaches including permutation, sampling, scaling, cropping, and jittering to create additional data by applying them on training data. With the augmented data, SensorAuth extracts sensor-based features in both time and frequency domains within a time window, then utilizes the one-class support vector machine to train the classifier, and finally authenticates users. We evaluate the authentication performance of SensorAuth in terms of the impact of window size, accuracy on each of and combinations of data augmentation approaches, time efficiency, energy consumption, and comparisons with the representative classifiers and with the existing approaches, respectively. The experimental results show that SensorAuth performs highly accurate and time-efficient continuous authentication, by reaching the lowest median equal error rate of 4.66%, and consuming a short authentication time of approximately 5 s.