Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning
This work addresses user authentication in mobile devices, but it is incremental as it applies existing methods to a new dataset.
The research tackled continuous authentication by creating a novel dataset of touch gestures from 15 users playing Minecraft and used machine learning classifiers, with SVC achieving about 90% accuracy to distinguish users based on touch dynamics.
This research aims to further understanding in the field of continuous authentication using behavioral biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems