Machine Learning Algorithms In User Authentication Schemes
It addresses security risks in mobile devices by reviewing existing methods, but is incremental as it builds on previous work without introducing new results.
This study provides a comprehensive overview of machine learning algorithms used in user authentication schemes based on touch dynamics and device movement, discussing their benefits, limitations, and future suggestions.
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.