This project takes a look at a biometric classifier with keystroke dynamics. Besides typing a password to validate a user, it is possible to discriminate users by their typing rhythms. This features can be used to detect impostors. This project is based on the data set of the publication Killourhy, K. S., & Maxion, R. A. Comparing anomaly detectors for keystroke dynamics. In Proc. of the Int. Conf. on Dependable Systems and Networks (DSN) (pp. 125-134). The objective is to build a classification baser on neural networks with the keras library.
The data set consists of 51 subject each typing the password .tie5Roanl 400 times. The measures collected are the hold time (H), keydown-keydown time (DD) and keyup-keydown time (UD).
This project is licensed under the MIT license. See the LICENSE for details