Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms

Abstract

In our world of growing machine intelligence and increasing security risks, there is a dire need for authentication to be liberated from password dependency and restrictions. This paper discusses the implementation of keystrokebiometrics to enhance security using machine-learning algorithms on both Windows and Android. Our research analyzesa user’s behavior for authorization purposes by capturing the user’s typing pattern. The system extracts several featuresfrom the user’s typing pattern to apply unary classification for user behavior analysis so that we can detect unauthorizedusers. Our system implements machine learning on tap dynamics in Android, allowing both training and prediction andovercoming its computational restrictions.