Keystroke dynamics based user authentication using numeric keypad

Keystroke dynamics is the study to identify/authenticate a person based on his/her typing rhythms, which are inferred from keystroke events like key-press and key-release. A lot of research work has been done in this field where the researchers have used either only alphabetic or alphanumeric or only numeric inputs. In this paper we address the question — What is the best possible numeric input for authentication using keystroke dynamics. We accomplished this by making the users enter four different numbers. Each number consisted of 8-digits. Out of these four numbers two were random numbers while the other two were formed using digits which had some pattern to them. Random Forest and Naive Bayes were used as classifiers. The results showed that using Random Forest classifier yielded best results when a random number is taken as input. The study also proved that a combination of hold time and latency as features yielded improved results. We achieved an average false acceptance rate of 2.7% and false rejection rate of 35.9%.