Keystroke Dynamics for User Identification
Atharva Sharma Martin Jureček Mark Stamp
In previous research, keystroke dynamics has shown promise for user
authentication, based on both fixed-text and free-text data. In this research,
we consider the more challenging multiclass user identification problem, based
on free-text data. We experiment with a complex image-like feature that has
previously been used to achieve state-of-the-art authentication results over
free-text data. Using this image-like feature and multiclass Convolutional
Neural Networks, we are able to obtain a classification (i.e., identification)
accuracy of 0.78 over a set of 148 users. However, we find that a Random Forest
classifier trained on a slightly modified version of this same feature yields
an accuracy of 0.93.