Skip to main content
SpringerLink
Log in

Computer User Profiling Based on Keystroke Analysis

  • Chapter
  • First Online:
Book cover Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 395))

Abstract

The article concerns the issues related to a computer user verification based on the analysis of a keyboard activity in a computer system. The research focuses on the analysis of a user’s continuous work in a computer system, which constitutes a type of a free-text analysis. To ensure a high level of a users’ data protection, an encryption of keystrokes was implemented. A new method of a computer user profiling based on encrypted keystrokes is introduced. Additionally, an attempt to an intrusion detection based on the \( k \)-NN classifier is performed.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Kudłacik, P., Porwik, P.: A new approach to signature recognition using the fuzzy method. Pattern Anal. Appl. 17(3), 451–463 (2014). doi:10.1007/s10044-012-0283-9

    Article  MathSciNet  MATH  Google Scholar 

  2. Kudłacik, P., Porwik, P., Wesołowski, T.: Fuzzy Approach for Intrusion Detection Based on User’s Commands. Soft Computing, Springer, Berlin Heidelberg (2015), doi:10.1007/s00500-015-1669-6

    Google Scholar 

  3. Pałys, M., Doroz, R., Porwik, P.: On-line signature recognition based on an analysis of dynamic feature. In: IEEE International Conference on Biometrics and Kansei Engineering, pp. 103–107, Tokyo Metropolitan University Akihabara (2013)

    Google Scholar 

  4. Porwik, P., Doroz, R., Orczyk, T.: The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition. Pattern Analysis and Applications, doi:10.1007/s10044-014-0419-1

    Google Scholar 

  5. Wesołowski, T., Pałys, M., Kudłacik, P.: computer user verification based on mouse activity analysis. Stud. Comput. Intell. 598, 61–70 (2015). Springer International Publishing

    Google Scholar 

  6. Alsultan, A., Warwick, K.: Keystroke dynamics authentication: a survey of free-text methods. J. Comput. Sci. Issues 10(1) 1–10 (2013) (Issue 4)

    Google Scholar 

  7. Araujo, L.C.F., Sucupira Jr., L.H.R., Lizarraga, M.G., Ling, L.L., Yabu-Uti, J.B.T.: User authentication through typing biometrics features. IEEE Trans. Signal Process. 53(2) 851–855 (2005)

    Google Scholar 

  8. Banerjee, S.P., Woodard, D.L.: Biometric authentication and identification using keystroke dynamics: a survey. J. Pattern Recognit. Res. 7, 116–139 (2012)

    Article  Google Scholar 

  9. Teh, P.S., Teoh, A.B.J., Yue, S.: A survey of keystroke dynamics biometrics. Sci. World J. 2013, Article ID: 408280, 24 pp. (2013) doi:10.1155/2013/408280

    Google Scholar 

  10. Zhong, Y., Deng, Y., Jain, A.K.: Keystroke dynamics for user authentication. In: IEEE Computer Society Conference, Computer Vision and Pattern Recognition Workshops, pp. 117–123 (2012), doi:10.1109/CVPRW.2012.6239225

  11. Raiyn, J.: A survey of cyber attack detection strategies. Int. J. Secur. Its Appl. 8(1), 247–256 (2014)

    Google Scholar 

  12. Salem, M.B., Hershkop, S., Stolfo, S.J.: A survey of insider attack detection research. Adv. Inf. Secur. 39, 69–90, Springer US (2008)

    Google Scholar 

  13. Dowland, P.S., Singh, H., Furnell, S.M.: A preliminary investigation of user authentication using continuous keystroke analysis. In: The 8th Annual Working Conference on Information Security Management and Small Systems Security (2001)

    Google Scholar 

  14. Saha, J., Chaki, R.: An Approach to Classify Keystroke Patterns for Remote User Authentication. J. Med. Inf. Technol. 23, 141–148 (2014)

    Google Scholar 

  15. Lopatka, M., Peetz, M.: Vibration sensitive keystroke analysis. In: Proceedings of the 18th Annual Belgian-Dutch Conference on Machine Learning, pp. 75–80 (2009)

    Google Scholar 

  16. Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: International Conference on Dependable Systems and Networks (DSN-09), pp. 125–134. IEEE Computer Society Press (2009)

    Google Scholar 

  17. Rybnik, M., Tabedzki, M., Adamski, M., Saeed, K.: An exploration of keystroke dynamics authentication using non-fixed text of various length, In: IEEE International Conference on Biometrics and Kansei Engineering, pp. 245–250 (2013)

    Google Scholar 

  18. Tappert, C.C., Villiani, M., Cha, S.: Keystroke biometric identification and authentication on long-text input. In: Wang, L., Geng, X. (eds.) Behavioral Biometrics for Human Identification: Intelligent Applications, pp. 342–367 (2010), doi:10.4018/978-1-60566-725-6.ch016

  19. Gunetti, D., Picardi, C., Ruffo, G.: Keystroke analysis of different languages: a case study. Adv. Intell. Data Anal. 3646, 133–144 (2005)

    MATH  Google Scholar 

  20. Foster, K.R., Koprowski, R., Skufca, J.D.: Machine learning, medical diagnosis, and biomedical engineering research—commentary. Biomed. Eng. Online 13, 94 (2014), doi:10.1186/1475-925X-13-94

    Google Scholar 

  21. Hu, J., Gingrich, D., Sentosa, A.: A K-nearest Neighbor Approach for User Authentication through Biometric Keystroke Dynamics. In: IEEE International Conference on Communications, pp. 1556–1560 (2008)

    Google Scholar 

  22. Filho, J.R.M., Freire, E.O.: On the equalization of keystroke timing histogram. Pattern Recognit. Lett. 27(13), 1440–1446 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

The research described in this article has been partially supported from the funds of the project “DoktoRIS—Scholarship program for innovative Silesia” co-financed by the European Union under the European Social Fund.

Author information

Authors and Affiliations

  1. University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200, Sosnowiec, Poland

    Tomasz Emanuel Wesołowski & Piotr Porwik

Authors
  1. Tomasz Emanuel Wesołowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Porwik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomasz Emanuel Wesołowski .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Università Ca' Foscari, Venice, Italy

    Agostino Cortesi

  3. Bialystok University of Technology Faculty of Computer Science, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this chapter

Cite this chapter

Wesołowski, T.E., Porwik, P. (2016). Computer User Profiling Based on Keystroke Analysis. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2650-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2650-5_1

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2648-2

  • Online ISBN: 978-81-322-2650-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation