Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning

TL;DR

This study introduces a new dataset of user gestures during Minecraft gameplay on tablets and evaluates machine learning classifiers, achieving around 90% accuracy in continuous user authentication based on touch dynamics.

Contribution

The paper presents a novel dataset of gesture data for continuous authentication and evaluates ML classifiers, highlighting the effectiveness of SVC in distinguishing users.

Findings

SVC achieved ~90% accuracy in user authentication.

Touch dynamics can effectively differentiate users.

Further research needed for real-world deployment.

Abstract

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