  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.
