Keystroke Dynamics Based Authentication System

Abstract

Nowadays, many companies use biometric technologies for the security of critical systems as well as username-password methods. In the literature, biometric systems are the most commonly used systems among the two-factor authentication systems. There are two different approaches to biometric systems: physical and behavioral biometric systems. In the last decade, the accuracy of behavioral biometric systems has significantly increased with the use of machine learning methods in these systems. For this reason, the usage areas of the studies in this field have expanded. In this study, we focus on keystroke dynamics based on behavioral methods. Firstly, we make a web application to collect keystroke data from 54 employees in a company. Then, we use the benchmark database and our database to train-test machine learning algorithms, which have the highest accuracy in this field in the literature. Among them, tree-based algorithms have the highest accuracy score, with an average of 0.94.