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

Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning