  Mobile devices have been manufactured and enhanced at growing rates in the
past decades. While this growth has significantly evolved the capability of
these devices, their security has been falling behind. This contrast in
development between capability and security of mobile devices is a significant
problem with the sensitive information of the public at risk. Continuing the
previous work in this field, this study identifies key Machine Learning
algorithms currently being used for behavioral biometric mobile authentication
schemes and aims to provide a comprehensive review of these algorithms when
used with touch dynamics and phone movement. Throughout this paper the
benefits, limitations, and recommendations for future work will be discussed.
