Continuous User Authentication using Keystroke Dynamics for Touch Devices

An authenticate users have increased due to failures in traditional authentication systems. Keystroke dynamics-based authentication is one of the most secure behavioral biometric authentication systems. This study aims to research and implement a non-fool proof, low-cost continuous authentication system for touch devices based on keystroke dynamics. A custom-developed mobile application was used to collect users’ keystroke dynamics. Bigrams were used as input parameters. 2 artificial neural networks were used in this study. The first network was used to identify users’ handedness, while the second one decided to use validity. Also, input was not limited, and users could type free text. As a result, overall accuracy was above 83.74%. Based on the results, we concluded that keystroke dynamics could be used for continuous user authentication purposes even with freely typed tests.