  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
