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Mobile devices have been manufactured and enhanced at growing rates in thepast decades. While this growth has significantly evolved the capability ofthese devices, their security has been falling behind. This contrast indevelopment between capability and security of mobile devices is a significantproblem with the sensitive information of the public at risk. Continuing theprevious work in this field, this study identifies key Machine Learningalgorithms currently being used for behavioral biometric mobile authenticationschemes and aims to provide a comprehensive review of these algorithms whenused with touch dynamics and phone movement. Throughout this paper thebenefits, limitations, and recommendations for future work will be discussed.

Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication