Skip to main content
SpringerLink
Log in
Book cover

Information Technology and Applied Mathematics pp 69–83Cite as

Analysis of Typing Pattern in Identifying Soft Biometric Information and Its Impact in User Recognition

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 699))

Abstract

As of now, the performance of keystroke dynamics biometric in user recognition is not acceptable in practice due to intra-class variations, high failure to enroll rate (FER) or various troubles in data acquisition methods or diverse use of sensing devices. As per the previous study, the performance of this technique can be improved by incorporation of gender information, a soft biometric characteristic, extracted from the typing pattern on a computer keyboard that provides some additional information about the user. This soft biometric trait has low user discriminating power but can be used to enhance the performance of user recognition in accuracy and time efficiency. Furthermore, it has been observed that the age group (18–30/30+ or <18/18+), gender (male/female), handedness (left-handed/right-handed), hand(s) used (one hand/both hands), typing skill (touch/others), and emotional states (anger/excitation) can be extracted from the way of typing on a computer keyboard for single predefined text. In this paper, we are interested in identifying multiple soft biometric traits using two leading machine learning methods: support vector machine with radial basis function (SVM-RBF) and fuzzy-rough nearest neighbor with vaguely quantified rough set (FRNN-VQRS) on multiple publicly available authentic and recognized keystroke dynamics datasets collected through a computer keyboard as well as touchscreen phone. The performance of machine learning methods are changed significantly in changing dataset in keystroke dynamics domain, but the evaluation performance of FRNN-VQRS in our experiment is promising and consistent in identifying traits. At the end, the impacts of the incorporation of soft biometric traits with primary biometric characteristics in user recognition are presented and compared the evaluation performance of nine anomaly detectors.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Roy, S., Roy, U., Sinha, D.D.: Free-text user authentication technique through keystroke dynamics. In: 2014 International Conference of High Performing Computer Application ICHPCA 2014 (2015)

    Google Scholar 

  2. CENELEC, alarm systems—Access control systems for use in security applications—part 1. In: System Requirements, EN 50133-1 edition (1996)

    Google Scholar 

  3. Zulkarnain, S., et al.: Soft biometrics for keystroke dynamics: profiling individuals while typing passwords. Comput. Secur. (2014)

    Google Scholar 

  4. Epp, C., Lippold, M., Mandryk, R.L.: Identifying emotional states using keystroke dynamics. In: Proceedings of SIGCHI Conference Human Factors Computer System, pp. 715–724 (2011)

    Google Scholar 

  5. Roy, S., Roy, U., Sinha, D.D.: ACO-random forest approach to protect the kids from internet threats through keystroke. Int. J. Eng. Technol. 2–9 (2017) (Accepted)

    Google Scholar 

  6. Uzun, Y., Bicakci, K., Uzunay, Y.: Could We Distinguish Child Users from Adults Using Keystroke Dynamics? (2014)

    Google Scholar 

  7. Gaines, R.S., Lisowski, W., Press, S.J., Shapiro, N.: Authentication by Keystroke Timing: Some Preliminary Results, in Technical Report R-2526-NSF. Rand Corporation, May (1980)

    Google Scholar 

  8. Jain, A., Nandakumar, K., Lu, X., Park, U.: Integrating faces, fingerprints, and soft biometric traits for user recognition. Biometr. Authenticat. no. May, 259–269 (2004)

    Google Scholar 

  9. Frank, E., Hall, M.A., Witten, I.H.: The Weka Workbench Data Mining Practical Machine Learning Tools and Techniques, 4th ed. (1999)

    Google Scholar 

  10. Giot, R., Rosenberger, C.: A new soft biometric approach for keystroke dynamics based on gender recognition. Int. J. Inf. Technol. Manag. Spec. Issue Adv. Trends Biometric. 11(August), 1–16 (2012)

    Google Scholar 

  11. Idrus, S.Z.S., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics for keystroke dynamics. In: 10th International Conference on Image Analysis and Recognition (ICIAR), pp. 11–18 (2013)

    Google Scholar 

  12. Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: Proceedings of the International Conference on Dependable Systems and Networks, pp. 125–134 (2009)

    Google Scholar 

  13. El-Abed, M., Dafer, M., El Khayat, R.: RHU keystroke: a mobile-based benchmark for keystroke dynamics systems. In: 2014 International Carnahan Conference Secured Technology, pp. 1–4 (2014)

    Google Scholar 

  14. Antal, M., Szabo, L.Z.: An evaluation of one-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices. In: Proceedings of 2015 20th International Conference Control System Computer Science CSCS 2015, pp. 343–350 (2015)

    Google Scholar 

  15. Loy, C.C., Lim, C.P., Lai, W.K.: Pressure-based typing biometrics user authentication using the fuzzy ARTMAP neural network. In: International Conference on Neural Information Processing (ICONIP) (2005)

    Google Scholar 

  16. Roth, J., Liu, X., Ross, A., Metaxas, D.: Biometric authentication via keystroke sound. In: 2013 International Conference on Biometrics, pp. 1–8 (2013)

    Google Scholar 

  17. Montalvão, J., Freire, E.O., Bezerra, M.A., Garcia, R.: Contributions to empirical analysis of keystroke dynamics in passwords. Pattern Recogn. Lett. (2015)

    Google Scholar 

  18. Bello, L., Bertacchini, M.: Collection and publication of a fixed text keystroke dynamics dataset. In: CACIC 2010—XVI Argentino Ciencias LA Congress of Computer Science, pp. 822–831 (2010)

    Google Scholar 

  19. Idrus, S.Z.S., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics database: a benchmark for keystroke dynamics biometric systems. In: 2013 International Conference of the Biometrics Special Interest Group (BIOSIG), 2013, no. September, pp. 1–8

    Google Scholar 

  20. Giot, R., El-Abed, M., Rosenberger, C.: Web-based benchmark for keystroke dynamics biometric systems: a statistical analysis. In: Intelligence of Information Hiding and Multimedia Signal Process, pp. 11–15 (2012)

    Google Scholar 

  21. Killourhy, K.S.: A scientific understanding of keystroke dynamics. Dr. Thesis, School of Computer Science, Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213, no. January, pp. 1–198 (2012)

    Google Scholar 

  22. Idrus, S.Z.S., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics database: a benchmark for keystroke dynamics biometric systems. In: 2013 International Conference of the Biometrics Special Interest Group (BIOSIG), September, pp. 1–8 (2013)

    Google Scholar 

  23. Jensen, R., Cornelis, C.: Fuzzy rough nearest neighbour classification and prediction. Theor. Comput. Sci. 412(42), 5871–5884 (2011)

    Article  MathSciNet  Google Scholar 

  24. Sarkar, M.: Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets Syst. 158(19), 2134–2152 (2007)

    Article  MathSciNet  Google Scholar 

  25. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. BJU Int. 101(1), 1396–1400 (2008)

    Google Scholar 

  26. Alghamdi, S.J., Elrefaei, L.A.: Dynamic user verification using touch keystroke based on medians vector proximity. In: Proceedings of 7th International Conference Computer Intelligence Communication System Networks, CICSyN 2015, pp. 121–126 (2015)

    Google Scholar 

  27. Zulkarnain, S., et al.: Keystroke Dynamics Performance Enhancement With Soft Biometrics

    Google Scholar 

  28. Roy, S., Roy, U., Sinha, D.D.: Comparative study of various features-mining-based classifiers in different keystroke dynamics datasets. In: Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems, vol. 2, pp. 155–164 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Calcutta, Kolkata, 700009, WB, India

    Soumen Roy & D. D. Sinha

  2. Visva-Bharati, Santiniketan, 731235, WB, India

    Utpal Roy

Authors
  1. Soumen Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Utpal Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. D. Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumen Roy .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Peeyush Chandra

  2. Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Debasis Giri

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Fagen Li

  4. Department of Mathematics, National Institute of Technology Durgapur, Durgapur, West Bengal, India

    Samarjit Kar

  5. Department of Applied Sciences, Haldia Institute of Technology, Haldia, West Bengal, India

    Dipak Kumar Jana

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Roy, S., Roy, U., Sinha, D.D. (2019). Analysis of Typing Pattern in Identifying Soft Biometric Information and Its Impact in User Recognition. In: Chandra, P., Giri, D., Li, F., Kar, S., Jana, D. (eds) Information Technology and Applied Mathematics. Advances in Intelligent Systems and Computing, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-7590-2_5

Download citation

Publish with us

Policies and ethics

Search

Navigation