Skip to main navigation menu Skip to main content Skip to site footer

Information technologies and systems

June 19, 2026; Cambridge, UK: X International Scientific and Practical Conference «EDUCATION AND SCIENCE OF TODAY: INTERSECTORAL ISSUES AND DEVELOPMENT OF SCIENCES»


HYBRID MACHINE LEARNING APPROACH FOR CONTINUOUS USER AUTHENTICATION VIA KEYSTROKE DYNAMICS IN INFOCOMMUNICATION SYSTEMS


DOI
https://doi.org/10.36074/logos-19.06.2026.034
Published
30.06.2026

Abstract

Modern infocommunication systems still rely mainly on static authentication methods such as passwords, PINs, and hardware tokens. Although these mechanisms are simple and widely used, they are vulnerable to brute-force attacks, phishing, credential stuffing, shoulder surfing, and credential theft. Once valid credentials are compromised, the system usually cannot verify whether the active user is legitimate, which creates a serious security gap in cloud-based and distributed environments.

References

  1. Mahfouz, A., Mahmoud, T. M., & Eldin, A. S. (2017). A survey on behavioral biometric authentication on smartphones. Journal of Information Security and Applications, 37, 28–37. https://doi.org/10.1016/j.jisa.2017.10.002
  2. Baig, A. F., & Eskeland, S. (2021). Security, privacy, and usability in continuous authentication: A survey. Sensors, 21(17), Article 5967. https://doi.org/10.3390/s21175967
  3. Monrose, F., & Rubin, A. D. (2000). Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems, 16(4), 351–359. https://doi.org/10.1016/S0167-739X(99)00059-X
  4. Joyce, R., & Gupta, G. (1990). Identity authorization based on keystroke latencies. Communications of the ACM, 33(2), 168–176. https://doi.org/10.1145/75577.75582
  5. Wang, X., & Hou, D. (2024). Enhancing keystroke dynamics authentication with ensemble learning and data resampling techniques. Electronics, 13(22), Article 4559. https://doi.org/10.3390/electronics13224559
  6. Arsh, A., Kar, N., Das, S., & Deb, S. (2024). Multiple approaches towards authentication using keystroke dynamics. Procedia Computer Science, 235, 2609–2618. https://doi.org/10.1016/j.procs.2024.04.246
  7. Ali, A. M., & Elrefaei, L. A. (2021). Keystroke dynamics-based user identification using Random Forest and other machine learning algorithms. Journal of Information Security and Applications, 58, Article 102787. https://doi.org/10.1016/j.jisa.2021.102787