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

• URL: http://arxiv.org/abs/2403.03832v1
• Date: Wed, 6 Mar 2024 16:22:49 GMT
• Title: Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning
• Authors: Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt, Rushit Dave
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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