YISROEL MIRSKY

Smartphones have become ubiquitous in our daily lives; they are used for a wide range of tasks and store increasing amounts of personal data. To minimize risk and prevent misuse of this data by unauthorized users, access must be restricted to verified users. Current classification-based methods for gesture-based user verification only consider single gestures, and not sequences. In this paper, we present a method which utilizes information from sequences of touchscreen gestures, and the context in which the gestures were made. To evaluate our approach, we built an application which records all the necessary data from the device (touch and contextual sensors which do not consume significant battery life), and installed it on several Galaxy S4 smartphones. The smartphones were given to 20 volunteers to use as their personal phones for two-weeks. Using XGBoost on the collected data, we were able to classify between a legitimate user and the population of illegitimate users (imposters) with an average equal error rate (EER) of 4.78% and an average area under the curve (AUC) of 98.15%. Our method demonstrates that by considering sequences of gestures, as opposed to individual gestures, the accuracy of the verification process improves significantly.