Mini-Batch Ensemble Method on Keystroke Dynamics based User Authentication

  • Ho, Jiacang (Department of Ubiquitous IT, Graduate School, Dongseo University) ;
  • Kang, Dae-Ki (Department of Computer & Information Engineering, Dongseo University)
  • Received : 2016.07.17
  • Accepted : 2016.08.05
  • Published : 2016.09.30


The internet allows the information to flow at anywhere in anytime easily. Unfortunately, the network also becomes a great tool for the criminals to operate cybercrimes such as identity theft. To prevent the issue, using a very complex password is not a very encouraging method. Alternatively, keystroke dynamics helps the user to solve the problem. Keystroke dynamics is the information of timing details when a user presses a key or releases a key. A machine can learn a user typing behavior from the information integrate with a proper machine learning algorithm. In this paper, we have proposed mini-batch ensemble (MIBE) method which does the preprocessing on the original dataset and then produces multiple mini batches in the end. The mini batches are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm.


Mini-batch;ensemble method;keystroke dynamics;user authentication


Supported by : National Research Foundation of Korea (NRF)


  1. M. M. Al-Jarrah, "An anomaly detector for keystroke dynamics based on medians vector proximity," Journal of Emerging Trends in Computing and Information Sciences, vol. 3, no. 6, pp. 988-993, 2012.
  2. S. Cho, C. Han, D. H. Han, and H.-I. Kim, "Web-based keystroke dynamics identity verification using neural network," Journal of organizational computing and electronic commerce, vol. 10, no. 4, pp. 295-307, 2000.
  3. R. Giot, M. El-Abed, and C. Rosenberger, "Web-based benchmark for keystroke dynamics biometric systems: A statistical analysis," in Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), 2012 Eighth International Conference on. IEEE, 2012, pp. 11-15.
  4. S. Z. S. Idrus, E. Cherrier, C. Rosenberger, and P. Bours, "Soft biometrics for keystroke dynamics," in Image analysis and recognition. Springer, 2013, pp. 11-18.
  5. K. S. Killourhy, R. Maxion et al., "Comparing anomaly-detection algorithms for keystroke dynamics," in Dependable Systems & Networks, 2009. DSN'09. IEEE/IFIP International Conference on. IEEE, 2009, pp. 125-134.
  6. J. Montalvao, E. O. Freire, M. A. Bezerra Jr, and R. Garcia, "Contributions to empirical analysis of keystroke dynamics in passwords," Pattern Recognition Letters, vol. 52, pp. 80-86, 2015.
  7. K. Revett, "A bioinformatics based approach to user authentication via keystroke dynamics," International Journal of Control, Automation and Systems, vol. 7, no. 1, pp. 7-15, 2009.
  8. Z. Syed, S. Banerjee, and B. Cukic, "Leveraging variations in event sequences in keystroke-dynamics authentication systems," in High- Assurance Systems Engineering (HASE), 2014 IEEE 15th International Symposium on. IEEE, 2014, pp. 9-16.
  9. X. Wang, F. Guo, and J.-f. Ma, "User authentication via keystroke dynamics based on difference subspace and slope correlation degree," Digital Signal Processing, vol. 22, no. 5, pp. 707-712, 2012.
  10. E. Yu and S. Cho, "Keystroke dynamics identity verification - its problems and practical solutions," Computers & Security, vol. 23, no. 5, pp. 428-440, 2004.
  11. R. Moskovitch, C. Feher, A. Messerman, N. Kirschnick, T. Mustafic, A. Camtepe, B. Lohlein, U. Heister, S. Moller, L. Rokach et al., "Identity theft, computers and behavioral biometrics," in Intelligence and Security Informatics, 2009. ISI'09. IEEE International Conference on. IEEE, 2009, pp. 155-160.
  12. A. N. H. Nahin, J. M. Alam, H. Mahmud, and K. Hasan, "Identifying emotion by keystroke dynamics and text pattern analysis," Behaviour & Information Technology, vol. 33, no. 9, pp. 987-996, 2014.
  13. R. Giot and C. Rosenberger, "A new soft biometric approach for keystroke dynamics based on gender recognition," International Journal of Information Technology and Management, vol. 11, no. 1-2, pp. 35-49, 2012.
  14. T. G. Dietterich, "Ensemble methods in machine learning," in Multiple classifier systems. Springer, 2000, pp. 1-15.
  15. J. Ho and D.-K. Kang, "Sequence alignment with dynamic divisor generation for keystroke dynamics based user authentication," Journal of Sensors, vol. 2015, 2015.