Keystroke Dynamics for User Identification

Atharva Sharma, Martin Jureček and Mark Stamp ()
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Atharva Sharma: San Jose State University
Martin Jureček: Czech Technical University in Prague
Mark Stamp: San Jose State University

A chapter in Machine Learning, Deep Learning and AI for Cybersecurity, 2025, pp 601-622 from Springer

Abstract: Abstract 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, in the case of 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 attain a classification (i.e., identification) accuracy of 0.78 over a set of 148 users. Surprisingly, we find that a Random Forest classifier trained on a slightly modified version of this same feature yields an improved accuracy of 0.93.

Date: 2025
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DOI: 10.1007/978-3-031-83157-7_21

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