  In recent years the amount of secure information being stored on mobile
devices has grown exponentially. However, current security schemas for mobile
devices such as physiological biometrics and passwords are not secure enough to
protect this information. Behavioral biometrics have been heavily researched as
a possible solution to this security deficiency for mobile devices. This study
aims to contribute to this innovative research by evaluating the performance of
a multimodal behavioral biometric based user authentication scheme using touch
dynamics and phone movement. This study uses a fusion of two popular publicly
available datasets the Hand Movement Orientation and Grasp dataset and the
BioIdent dataset. This study evaluates our model performance using three common
machine learning algorithms which are Random Forest Support Vector Machine and
K-Nearest Neighbor reaching accuracy rates as high as 82% with each algorithm
performing respectively for all success metrics reported.
