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Keystroke Dynamics for User Identification (2307.05529v1)

Published 7 Jul 2023 in cs.LG and cs.CR

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, 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.

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References (28)
  1. Biometric recognition based on free-text keystroke dynamics. IEEE Transactions on Cybernetics, 44(4):458–472, 2014.
  2. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology, ICET, pages 1–6, 2017.
  3. Ross J. Anderson. Security Engineering: A Guide to Building Dependable Distributed Systems. John Wiley & Sons, Inc., 2001.
  4. Tewari Anurag. An improved user identification based on keystroke-dynamics and transfer learning. Webology, 19:5369–5387, 01 2022.
  5. A random forest guided tour. Test, 25(2):197–227, 2016.
  6. Computer-access security systems using keystroke dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(12):1217–1222, 1990.
  7. Improved regularization of convolutional neural networks with cutout. https://arxiv.org/abs/1708.04552, 2017.
  8. Authentication by keystroke timing: Some preliminary results. Technical Report R-2526-NSF, RAND Corporation, 1980. https://www.rand.org/pubs/reports/R2526.html.
  9. Keystroke dynamics with low constraints SVM based passphrase enrollment. In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pages 1–6, 2009.
  10. Support vector machines. IEEE Intelligent Systems and their Applications, 13(4):18–28, 1998.
  11. A k𝑘kitalic_k-nearest neighbor approach for user authentication through biometric keystroke dynamics. In 2008 IEEE International Conference on Communications, pages 1556–1560, 2008.
  12. Account-sharing detection through keystroke dynamics analysis. International Journal of Electronic Commerce, 14(2):109–126, 2009.
  13. Comparing anomaly-detection algorithms for keystroke dynamics. In 2009 IEEE/IFIP International Conference on Dependable Systems and Networks, DSN, pages 125–134, 2009.
  14. Marc Kowtko. Biometric authentication for older adults. In 2014 IEEE Long Island Systems, Applications and Technology Conference, LISAT, pages 1–6, 2014.
  15. Free-text keystroke dynamics for user authentication. In Mark Stamp, Corrado Aaron Visaggio, Francesco Mercaldo, and Fabio Di Troia, editors, Artificial Intelligence for Cybersecurity, pages 357–380. Springer, 2022.
  16. User keystroke authentication based on convolutional neural network. In Ilsun You, Hsing-Chung Chen, Vishal Sharma, and Igor Kotenko, editors, Mobile Internet Security, pages 157–168, 2019.
  17. Keystroke dynamics classification based on lstm and blstm models. In 2021 International Conference on Cyberworlds, CW, pages 295–298, 2021.
  18. On the equalization of keystroke timing histograms. Pattern Recognition Letters, 27(13):1440–1446, 2006.
  19. Keystroke dynamics based user authentication using deep learning neural networks. In 2022 International Conference on Cyberworlds, CW, pages 220–227, 2022.
  20. Keystroke user recognition through extreme learning machine and evolving cluster method. In 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC, pages 1–5, 2015.
  21. A survey of user authentication based on mouse dynamics. In Global E-Security, pages 210–219, 2008.
  22. Computer user verification using login string keystroke dynamics. IEEE Transactions on Systems, Man, and Cybernetics — Part A: Systems and Humans, 28(2):236–241, 1998.
  23. User authentication using combination of behavioral biometrics over the touchpad acting like touch screen of mobile device. In 2008 International Conference on Computer and Electrical Engineering, pages 82–86, 2008.
  24. Usable biometrics for an ageing population. In Michael Fairhurst, editor, Age Factors in Biometric Processing. Institution of Engineering and Technology, 2013.
  25. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014.
  26. A study of users with visual disabilities and a fingerprint process. Technical Report NISTIR 7435, National Institute of Standards and Technology, 2008.
  27. Shared keystroke dataset for continuous authentication. In 2016 IEEE International Workshop on Information Forensics and Security, WIFS, pages 1–6, 2016.
  28. Continuous authentication by free-text keystroke based on CNN plus RNN. Procedia Computer Science, 147:314–318, 2019. 2018 International Conference on Identification, Information and Knowledge in the Internet of Things.

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