Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning
<|reference_start|>Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning: 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<|reference_end|>
@article{nascimento2024your,
    title={Your device may know you better than you know yourself -- continuous
  authentication on novel dataset using machine learning},
    author={Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary
  Winterfeldt, Rushit Dave},
    journal={arXiv preprint arXiv:2403.03832},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03832},
    primaryClass={cs.AI}
}