Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916149030027264 |
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| author | Nascimento, Pedro Gomes do Witiak, Pidge MacCallum, Tucker Winterfeldt, Zachary Dave, Rushit |
| author_facet | Nascimento, Pedro Gomes do Witiak, Pidge MacCallum, Tucker Winterfeldt, Zachary Dave, Rushit |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning Nascimento, Pedro Gomes do Witiak, Pidge MacCallum, Tucker Winterfeldt, Zachary Dave, Rushit Artificial Intelligence 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 |
| title | Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.03832 |