Behavioral Biometrics Based User Authentication Schemes Using Machine Learning

Abstract

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 multi-modal 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 (HMOG) dataset and the BioIdent dataset. This study evaluates our model’s performance using three common machine learning algorithms. 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.