Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication
This is an incremental review paper that addresses security gaps in mobile devices for the general public.
This study provides a comprehensive review of machine learning algorithms used for behavioral biometric authentication on mobile devices, focusing on touch dynamics and phone movement data, discussing their benefits, limitations, and future recommendations.
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.