Skip to main content
SpringerLink
Log in

One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Biometric-based approaches, including keystroke dynamics on keyboards, mice, and mobile devices, have incorporated machine learning algorithms to learn users’ typing behavior for authentication systems. Among the machine learning algorithms, one-class naïve Bayes (ONENB) has been shown to be effective when it is applied to anomaly tests; however, there have been few studies on applying the ONENB algorithm to keystroke dynamics-based authentication. We applied the ONENB algorithm to calculate the likelihood of attributes in keystroke dynamics data. Additionally, we propose the speed inspection in typing skills (SITS) algorithm designed from the observation that every person has a different typing speed on specific keys. These specific characteristics, also known as the keystroke’s index order, can be used as essential patterns for authentication systems to distinguish between a genuine user and imposter. To further evaluate the effectiveness of the SITS algorithm and examine the quality of each attribute type (e.g., dwell time and flight time), we investigated the influence of attribute types on the keystroke’s index order. From the experimental results of the proposed algorithms and their combination, we observed that the shortest/longest time attributes and separation of the attributes are useful for enhancing the performance of the proposed algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Al-Jarrah MM (2012) An anomaly detector for keystroke dynamics based on medians vector proximity. J Emerg Trends Comput Inf Sci 3(6):988–993

    Google Scholar 

  2. Alsultan A, Warwick K, Wei H (2017) Non-conventional keystroke dynamics for user authentication. Pattern Recogn Lett 89:53–59

    Article  Google Scholar 

  3. Banerjee S, Woodard DL (2012) Biometric authentication and identification using keystroke dynamics: A survey. J Pattern Recogn Res 7(1):116–139

    Article  Google Scholar 

  4. Bergadano F, Gunetti D, Picardi C (2002) User authentication through keystroke dynamics. ACM Trans Inf Syst Secur (TISSEC) 5(4):367–397

    Article  Google Scholar 

  5. Bleha S, Knopp J, Obaidat M (1992) Performance of the perceptron algorithm for the classification of computer users. In: Proceedings of the 1992 ACM/SIGAPP Symposium on Applied Computing: Technological Challenges of the 1990’s. ACM, pp 863–866

  6. Bleha SA, Obaidat M (1993) Computer users verification using the perceptron algorithm. IEEE Trans Syst Man Cybern 23(3):900–902

    Article  Google Scholar 

  7. Brown M, Rogers SJ (1994) A practical approach to user authentication. In: Proceeding of the 10t h Annual IEEE Computer Security Applications Conference. IEEE, pp 108–116

  8. D’Agostini G (1995) A multidimensional unfolding method based on Bayes theorem. Nuclear Instrum Methods Phys Res Sect A: Accelerators, Spectrometers Detectors Assoc Equip 362(2):487–498

    Article  Google Scholar 

  9. Gaines RS, Lisowski W, Press SJ, Shapiro N (1980) Authentication by keystroke timing: Some preliminary results. Technical report, DTIC Document

  10. Giot R, El-Abed M, Rosenberger C (2012) Web-based benchmark for keystroke dynamics biometric systems: A statistical analysis. In: Proceeding of the 8t h IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, pp 11–15

  11. Giot R, Rosenberger C (2012) A new soft biometric approach for keystroke dynamics based on gender recognition. Int J Inf Technol Manag 11(1-2):35–49

    Google Scholar 

  12. Guven A, Sogukpinar I (2003) Understanding users’ keystroke patterns for computer access security. Comput Secur 22(8):695–706

    Article  Google Scholar 

  13. Ho J, Kang DK (2014) Sequence alignment of dynamic intervals for keystroke dynamics based user authentication. In: Proceeding of the 7th IEEE International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, pp 1433–1438

  14. Ho J, Kang DK (2015) Sequence alignment with dynamic divisor generation for keystroke dynamics based user authentication. Journal of Sensors

  15. Hocquet S, Ramel JY, Cardot H (2005) Fusion of methods for keystroke dynamic authentication. In: Proceeding of the 4t h IEEE Workshop on Automatic Identification Advanced Technologies (AutoID’05). IEEE, pp 224–229

  16. Idrus SZS, Cherrier E, Rosenberger C, Bours P (2013) Soft biometrics for keystroke dynamics. In: Image Analysis and Recognition. Springer, pp 11–18

  17. Joyce R, Gupta G (1990) Identity authentication based on keystroke latencies. Commun ACM 33(2):168–176

    Article  Google Scholar 

  18. Kang P, Cho S (2015) Keystroke dynamics-based user authentication using long and free text strings from various input devices. Inform Sci 308:72–93

    Article  Google Scholar 

  19. Karnan M, Akila M, Krishnaraj N (2011) Biometric personal authentication using keystroke dynamics: A review. Appl Soft Comput 11(2):1565–1573

    Article  Google Scholar 

  20. Killourhy KS, Maxion RA (2009) Comparing anomaly-detection algorithms for keystroke dynamics. In: Proceeding of the IEEE/IFIP International Conference on Dependable Systems and Networks 2009. IEEE, pp 125–134

  21. Li-guo D, Peng D, Ai-ping L (2014) A new naive Bayes text classification algorithm. Telkomnika Indones J Electr Eng 12(2):947–952

    Google Scholar 

  22. Montalvão J, Freire EO, Bezerra MA Jr, Garcia R (2015) Contributions to empirical analysis of keystroke dynamics in passwords. Pattern Recogn Lett 52:80–86

    Article  Google Scholar 

  23. Moskovitch R, Feher C, Messerman A, Kirschnick N, Mustafić T, Camtepe A, Löhlein B, Heister U, Möller S, Rokach L et al (2009) Identity theft, computers and behavioral biometrics. In: Proceeding of the IEEE International Conference on Intelligence and Security Informatics, 2009. ISI’09. IEEE, pp 155–160

  24. Obaidat M, Macchairolo D (1994) A multilayer neural network system for computer access security. IEEE Trans Syst Man Cybern 24(5):806–813

    Article  Google Scholar 

  25. Obaidat M, Sadoun B (1997) Verification of computer users using keystroke dynamics. IEEE Trans Syst Man Cybern Part B (Cybern) 27(2):261–269

    Article  Google Scholar 

  26. Poss JC, Boye D, Mobley MW (2008) Biometric voice authentication. US Patent 7,386,448

  27. Revett K (2007) A bioinformatics based approach to behavioural biometrics. In: Proceeding of the Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007. IEEE, pp 665–670

  28. Revett K (2009) A bioinformatics based approach to user authentication via keystroke dynamics. Int J Control Autom Syst 7(1):7–15

    Article  Google Scholar 

  29. Syed Z, Banerjee S, Cukic B (2014) Leveraging variations in event sequences in keystroke-dynamics authentication systems. In: Proceeding of the 15th IEEE International Symposium on High-Assurance Systems Engineering. IEEE, pp 9–16

  30. Umphress D, Williams G (1985) Identity verification through keyboard characteristics. Int J Man-Mach Stud 23(3):263–273

    Article  Google Scholar 

  31. Wang X, Guo F, Ma JF (2012) User authentication via keystroke dynamics based on difference subspace and slope correlation degree. Digit Signal Process 22(5):707–712

    Article  MathSciNet  Google Scholar 

  32. Wesołowski TE, Porwik P, Doroz R (2016) Electronic health record security based on ensemble classification of keystroke dynamics. Appl Artif Intell 30(6):521–540

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Ubiquitous IT, Graduate School, Dongseo University, 47, Churye-Ro, Sasang-Gu, Busan, 47011, Republic of Korea

    Jiacang Ho

  2. Department of Computer and Information Engineering, Dongseo University, 47, Churye-Ro, Sasang-Gu, Busan, 47011, Republic of Korea

    Dae-Ki Kang

Authors
  1. Jiacang Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dae-Ki Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dae-Ki Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ho, J., Kang, DK. One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics. Appl Intell 48, 1547–1564 (2018). https://doi.org/10.1007/s10489-017-1020-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-017-1020-2

Keywords

Search

Navigation