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LSTM-based Free-Text Keystroke Dynamics for Continuous Authentication

by Benoit Azanguezet Q., Junie Toukem T., Elie Tagne Fute
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 5
Year of Publication: 2025
Authors: Benoit Azanguezet Q., Junie Toukem T., Elie Tagne Fute
10.5120/ijca2025924780

Benoit Azanguezet Q., Junie Toukem T., Elie Tagne Fute . LSTM-based Free-Text Keystroke Dynamics for Continuous Authentication. International Journal of Computer Applications. 187, 5 ( May 2025), 18-23. DOI=10.5120/ijca2025924780

@article{ 10.5120/ijca2025924780,
author = { Benoit Azanguezet Q., Junie Toukem T., Elie Tagne Fute },
title = { LSTM-based Free-Text Keystroke Dynamics for Continuous Authentication },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 5 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number5/lstm-based-free-text-keystroke-dynamics-for-continuous-authentication/ },
doi = { 10.5120/ijca2025924780 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-29T00:02:58.408619+05:30
%A Benoit Azanguezet Q.
%A Junie Toukem T.
%A Elie Tagne Fute
%T LSTM-based Free-Text Keystroke Dynamics for Continuous Authentication
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 5
%P 18-23
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We live in a fully networked world, with the ability to access digital information systems anytime, anywhere, using a variety of technological devices. This raises concerns about the security of systems and the protection of users’ personal information. The login/password pair is the most widely used authentication method in today’s information systems. However, the system becomes vulnerable if a third party obtains this information. Keystroke dynamics can be used as an additional layer of security to continuously check whether the person using the system is legitimate. In this research, we propose a continuous authentication model that uses the temporal and textual characteristics of a user’s keystroke dynamics based on a one-way LSTM. We have tested this approach on data collected from 8 users. The final result obtained by this model is promising, with an accuracy of 96.67%.

References
  1. D. Umphress and G. Williams. “Identity verification through keyboard characteristics”. In: International journal of man-machine studies 23.3 (1985), pages 263–273.
  2. S. Hochreiter and J. Schmidhuber. “Long short-term memory”. In: Neural computation 9.8 (1997), pages 1735–1780.
  3. F. Monrose and A. Rubin. “Authentication via keystroke dynamics”. In: Proceedings of the 4th ACM Conference on Computer and Communications Security. 1997, pages 48–56.
  4. V. Matyas and Z. Riha. “Toward reliable user authentication through biometrics”. In: IEEE Security & Privacy 1.3 (2003), pages 45–49.
  5. S. Prabhakar, S. Pankanti, and A. K. Jain. “Biometric recognition: Security and privacy concerns”. In: IEEE security & privacy 1.2 (2003), pages 33–42.
  6. A. Peacock, X. Ke, and M. Wilkerson. “Typing patterns: A key to user identification”. In: IEEE Security & Privacy 2.5 (2004), pages 40–47.
  7. K. Revett, H. Jahankhani, S. T. De Magalhaes, and H. M. Santos. “A survey of user authentication based on mouse dynamics”. In: Global E-Security: 4th International Conference, ICGeS 2008, London, UK, June 23-25, 2008. Proceedings. Springer. 2008, pages 210– 219.
  8. N. Kaushal and P. Kaushal. “Human identification and fingerprints: a review”. In: J biomet biostat 2.123 (2011), page 2.
  9. A. A. Ahmed and I. Traore. “Biometric Recognition Based on Free-Text Keystroke Dynamics”. In: IEEE Transactions on Cybernetics 44.4 (2014), pages 458–472.
  10. P. Bours and S. Mondal. “Continuous authentication with keystroke dynamics”. In: Norwegian Information Security Laboratory NISlab (2015), pages 41–58.
  11. P. Kang and S. Cho. “Keystroke dynamics-based user authentication using long and free text strings from various input devices”. In: Information Sciences 308 (2015), pages 72– 93.
  12. Y. Zhong and Y. Deng. “A survey on keystroke dynamics biometrics: approaches, advances, and evaluations”. In: Recent Advances in User Authentication Using Keystroke Dynamics Biometrics 1.1-22 (2015), page 2.
  13. M. De Marsico, A. Petrosino, and S. Ricciardi. “Iris recognition through machine learning techniques: A survey”. In: Pattern Recognition Letters 82 (2016), pages 106–115.
  14. A. Alsultan, K. Warwick, and H. Wei. “Non-conventional keystroke dynamics for user authentication”. In: Pattern Recognition Letters 89 (2017), pages 53–59.
  15. R. Oak and M. Khare. “A novel architecture for continuous authentication using behavioural biometrics”. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). IEEE. 2017, pages 767–771.
  16. A. Alsultan, K. Warwick, and H. Wei. “Improving the performance of free-text keystroke dynamics authentication by fusion”. In: Applied Soft Computing 70 (Sept. 2018), pages 1024– 1033.
  17. J. Kim, H. Kim, and P. Kang. “Keystroke dynamics-based user authentication using freely typed text based on user-adaptive feature extraction and novelty detection”. In: Applied Soft Computing 62 (2018), pages 1077–1087.
  18. J. Linden, R. Marquis, S. Bozza, and F. Taroni. “Dynamic signatures: A review of dynamic feature variation and forensic methodology”. In: Forensic science international 291 (2018), pages 216–229.
  19. B. Bhana and S. Flowerday. “Passphrase and keystroke dynamics authentication: Usable security”. In: Computers & Security 96 (2020), page 101925.
  20. A. T. Kiyani, A. Lasebae, and K. Ali. “Continuous user authentication based on deep neural networks”. In: 2020 International Conference on UK-China Emerging Technologies (UCET). IEEE. 2020, pages 1–4.
  21. Y. Kortli, M. Jridi, A. Al Falou, and M. Atri. “Face recognition systems: A survey”. In: Sensors 20.2 (2020), page 342.
  22. N. Raul, R. Shankarmani, and P. Joshi. “A comprehensive review of keystroke dynamicsbased authentication mechanism”. In: International Conference on Innovative Computing and Communications: Proceedings of ICICC 2019, Volume 2. Springer. 2020, pages 149– 162.
  23. S. Parkinson, S. Khan, A. Crampton, Q. Xu, W. Xie, N. Liu, and K. Dakin. “Password policy characteristics and keystroke biometric authentication”. In: IET Biometrics 10 (2021), pages 163–178.
  24. H.-C. Chang, J. Li, and M. Stamp. “Machine Learning-Based Analysis of Free-Text Keystroke Dynamics”. In: Artificial Intelligence for Cybersecurity. Springer, 2022, pages 331– 356.
  25. J. Li, H.-C. Chang, and M. Stamp. “Free-text keystroke dynamics for user authentication”. In: Artificial Intelligence for Cybersecurity. Springer, 2022, pages 357–380.
  26. Y. B. W. Piugie, J. Di Manno, C. Rosenberger, and C. Charrier. “Keystroke dynamics based user authentication using deep learning neural networks”. In: 2022 International Conference on Cyberworlds (CW). IEEE. 2022, pages 220–227.
Index Terms

Computer Science
Information Sciences

Keywords

Continuous authentication keystroke dynamics deep learning time series Identity verification.

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