International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Ensemble-By-Session 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 : 2016.09.12
  • Accepted : 2016.09.27
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

There are many free applications that need users to sign up before they can use the applications nowadays. It is difficult to choose a suitable password for your account. If the password is too complicated, then it is hard to remember it. However, it is easy to be intruded by other users if we use a very simple password. Therefore, biometric-based approach is one of the solutions to solve the issue. The biometric-based approach includes keystroke dynamics on keyboard, mice, or mobile devices, gait analysis and many more. The approach can integrate with any appropriate machine learning algorithm to learn a user typing behavior for authentication system. Preprocessing phase is one the important role to increase the performance of the algorithm. In this paper, we have proposed ensemble-by-session (EBS) method which to operate the preprocessing phase before the training phase. EBS distributes the dataset into multiple sub-datasets based on the session. In other words, we split the dataset into session by session instead of assemble them all into one dataset. If a session is considered as one day, then the sub-dataset has all the information on the particular day. Each sub-dataset will have different information for different day. The sub-datasets are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm after the preprocessing phase.

Keywords

References

  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. https://doi.org/10.1207/S15327744JOCE1004_07
  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. https://doi.org/10.1016/j.patrec.2014.09.016
  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. https://doi.org/10.1007/s12555-009-0102-2
  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. https://doi.org/10.1016/j.dsp.2012.04.012
  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. https://doi.org/10.1016/j.cose.2004.02.004
  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. https://doi.org/10.1080/0144929X.2014.907343
  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. https://doi.org/10.1504/IJITM.2012.044062
  14. T. G. Dietterich, "Ensemble methods in machine learning," in Multiple classifier systems. Springer, 2000, pp. 1-15.