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Keystroke Dynamics for User Identification

In: Machine Learning, Deep Learning and AI for Cybersecurity

Author

Listed:
  • Atharva Sharma

    (San Jose State University)

  • Martin Jureček

    (Czech Technical University in Prague)

  • Mark Stamp

    (San Jose State University)

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.

Suggested Citation

  • Atharva Sharma & Martin Jureček & Mark Stamp, 2025. "Keystroke Dynamics for User Identification," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 601-622, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-83157-7_21
    DOI: 10.1007/978-3-031-83157-7_21
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