Machine Learning Algorithms In User Authentication Schemes
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866909302680190976 |
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| author | Pryor, Laura Dave, Rushit Seliya, Naeem Boone, Evelyn R Sowells |
| author_facet | Pryor, Laura Dave, Rushit Seliya, Naeem Boone, Evelyn R Sowells |
| contents | In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different Machine Learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_07826 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Machine Learning Algorithms In User Authentication Schemes Pryor, Laura Dave, Rushit Seliya, Naeem Boone, Evelyn R Sowells Cryptography and Security Artificial Intelligence In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different Machine Learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper. |
| title | Machine Learning Algorithms In User Authentication Schemes |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2110.07826 |