Advertisement

Advances in Key Stroke Dynamics-Based Security Schemes

  • Mohammad S. Obaidat
  • P. Venkata KrishnaEmail author
  • V. Saritha
  • Shubham Agarwal
Chapter

Abstract

Securing access to computer and network systems has become an important issue in recent days because most of the people stockpile their important information on their cell phones, tablets, laptops, desktop computers, etc. Hence, it is highly essential to secure the human interaction with such systems and strengthen the presently being used authentication methods. Traditional authentication systems of using passwords provide a great deal of security, but these traditional systems do not provide enough security in the case of extensive use of computer networks and systems. Biometric-based security systems are proven to be successful in adding another layer of security to the traditional schemes that rely on passwords. Keystroke dynamics scheme provides a very feasible solution to identify/authenticate individuals over the computer network in a very effective manner. In addition, more advanced features of the smart phones provide much advantage to the keystroke dynamics as the authentication can be based on schemes like the finger print, its pressure, the area covered, etc. This chapter gives a review of all the underlying technologies behind the keystroke dynamics and their applications in different fields.

Keywords

Keystroke dynamics Computer security Network security Mouse dynamics Artificial neural networks Pattern recognition 

References

  1. 1.
    M.S. Obaidat, A methodology for improving computer access security. Comput. Secur. 12(7), 657–662 (1993)CrossRefGoogle Scholar
  2. 2.
    M.S. Obaidat, D.T. Macchiarolo, An online neural network system for computer access security. IEEE Trans. Ind. Electron. 40(2), 235–242 (1993).  https://doi.org/10.1109/41.222645CrossRefGoogle Scholar
  3. 3.
    M.S. Obaidat, D.T. Macchairolo, A multilayer neural network system for computer access security. IEEE Trans. Syst. Man Cybern. 24(5), 806–813 (1994)CrossRefGoogle Scholar
  4. 4.
    M.S. Obaidat, B. Sadoun, Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. 27(2), 261–269 (1997)CrossRefGoogle Scholar
  5. 5.
    M.S. Obaidat, B. Sadoun, Keystroke dynamics based authentication, in Biometrics: Personal Identification in Networked Society, ed. by A. Jain, R. Bolle, S. Pankanti (Eds), (Kluwer, Boston, 1999), pp. 213–230Google Scholar
  6. 6.
    M.S. Obaidat, N. Boudriga, Security of e-Systems and Computer Networks (Cambridge University Press, Cambridge, 2007)CrossRefGoogle Scholar
  7. 7.
    M. Antal, L.Z. Szabó, I. László, Keystroke dynamics on android platform. Procedia Technol. 19, 820–826 (2015)CrossRefGoogle Scholar
  8. 8.
    D. Stefan, D. Yao, Keystroke-dynamics authentication against synthetic forgeries, in Sixth International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2010), Chicago, IL, pp. 1–8, 2010Google Scholar
  9. 9.
    A. Abo-alian, N.L. Badr, M.F. Tolba, Keystroke dynamics-based user authentication service for cloud computing. Concurrency Comput. Pract. Exp. 28(9), 2567–2585 (2016)CrossRefGoogle Scholar
  10. 10.
    H. Çeker, S. Upadhyaya, Enhanced recognition of keystroke dynamics using Gaussian mixture models, in MILCOM 2015 - 2015 I.E. Military Communications Conference, Tampa, FL, pp. 13051310, 2015. doi:  https://doi.org/10.1109/MILCOM.2015.7357625
  11. 11.
    D.G. Brizan, A. Goodkind, P. Koch, K. Balagani, V.V. Phoha, A. Rosenberg, Utilizing linguistically enhanced keystroke dynamics to predict typist cognition and demographics. Int. J. Hum. Comput. Stud. 82, 57–68 (2015)CrossRefGoogle Scholar
  12. 12.
    M. Antal, L.Z. Szabó, An evaluation of one-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices, in 2015 20th International Conference on Control Systems and Computer Science, Bucharest, pp. 343–350, 2015. doi:  https://doi.org/10.1109/CSCS.2015.16
  13. 13.
    J.V. Monaco et al., One-handed keystroke biometric identification competition, 2015 International Conference on Biometrics (ICB), Phuket, pp. 58–64, 2015. doi:  https://doi.org/10.1109/ICB.2015.7139076
  14. 14.
    A. Morales, E. Luna-Garcia, J. Fierrez, J. Ortega-Garcia, Score normalization for keystroke dynamics biometrics, 2015 International Carnahan Conference on Security Technology (ICCST), Taipei, pp. 223–228, 2015. doi:  https://doi.org/10.1109/CCST.2015.7389686
  15. 15.
    D. Buschek, A. De Luca, F. Alt, Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen devices, Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, pp. 1393–1402, April 2015. doi:  https://doi.org/10.1145/2702123.2702252
  16. 16.
    P. Kang, S. Cho, Keystroke dynamics-based user authentication using long and free text strings from various input devices. Inf. Sci. 308, 72–93 (2015).  https://doi.org/10.1016/j.ins.2014.08.070CrossRefGoogle Scholar
  17. 17.
    I. Traore, I. Woungang, M.S. Obaidat, Y. Nakkabi, Online risk-based authentication using behavioral biometrics. Multimed. Tools Appl. J. 71(2), 575–605 (2014)CrossRefGoogle Scholar
  18. 18.
    M. Karnan, M. Akila, N. Krishnaraj, Biometric personal authentication using keystroke dynamics: a review. Appl. Soft Comput. 11(2), 1565–1573 (2011)CrossRefGoogle Scholar
  19. 19.
    I. Traore, I. Woungang, B. Khalilian, M.S. Obaidat, A. Ahmed, Dynamic sample size detection in learning command line sequence for continuous authentication. IEEE Trans. Syst. Man Cybern. B 42(5), 1343–1356 (2012)CrossRefGoogle Scholar
  20. 20.
    B. Sayed, I. Traore, I. Woungang, M.S. Obaidat, Biometric authentication using mouse gesture dynamics. IEEE Syst. J. 7(2), 262–274, Greece (2013)Google Scholar
  21. 21.
    A. Alsultan, K. Warwick, H. Wei, Non-conventional keystroke dynamics for user authentication. Pattern Recognit. Lett. 89, 53–59 (2017)CrossRefGoogle Scholar
  22. 22.
    D. Shanmugapriya, P. Ganapathi, A wrapper-based classification approach for personal identification through keystroke dynamics using soft computing techniques, in Identity Theft: Breakthroughs in Research and Practice, (IGI Global, Hershey, 2017), pp. 267–290. Web. 3 Jul 2017. doi:.  https://doi.org/10.4018/978-1-5225-0808-3.ch013CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Y. Zhong, Y. Deng, A survey on keystroke dynamics biometrics: approaches, advances, and evaluations, in Recent Advances in User Authentication Using Keystroke Dynamics Biometrics, (Science Gate Publishing, 2015), pp. 1–22Google Scholar
  25. 25.
    M.T.J. Modi, H.G. Upadhaya, M. Thakor, Password less authentication using keystroke dynamics a survey, in International Journal of Innovative Research in Computer and Communication Engineering, IJIRCCE, pp. 7060–7064, 2014Google Scholar
  26. 26.
    I.H. Shimaa, M.S. Mazen, H. Hala, User authentication with adaptive keystroke dynamics. Int. J. Comput. Sci. Issue (IJCSI) 10(4), pp. 126–134 (2013)Google Scholar
  27. 27.
    S. Cho, C. Han, D.H. Han, H. Kim, Web-based keystroke dynamics identity verification using neural network. J. Organ. Comput. Electron. Commer. 10(4), 295–307 (2000)Google Scholar
  28. 28.
    S. Hocquet, J. Ramel, H. Cardot, User classification for keystroke dynamics, in Advances in Biometrics, International Conference, ICB, pp. 531–539, 2007Google Scholar
  29. 29.
    S. Modi, S.J. Elliott, Keystroke dynamics verification using a spontaneously generated password, in Proceedings of the 40th Annual IEEE International Carnahan Conference on Security Technology (ICCST’06), pp. 116–121, Oct 2006Google Scholar
  30. 30.
    K. Revett, S. T. deMagalhaes, and H. M. D. Santos, “Enhancing login security through the use of keystroke input dynamics,” in Advances in Biometrics, Proceedings, vol. 3832, pp. 661–667, Springer, Berlin, Germany, 2006CrossRefGoogle Scholar
  31. 31.
    S.T. de Magalhaes, K. Revett, H.M.D. Santos, Password secured sites—stepping forward with keystroke dynamics, in Proceedings of the International Conference on Next Generation Web Services Practices (NWeSP’05), pp. 293–298, August 2005Google Scholar
  32. 32.
    M.M. Hoobi, Keystroke dynamics authentication based on Naïve Bayes classifier. Iraqi J. Sci. 56(2A), 1176–1184 (2015)Google Scholar
  33. 33.
    Z. Zainuddin, A.S. Laswi, Implementation of the LDA algorithm for online validation Based on face recognition. J. Phys. Conf. Ser. 801(1), pp. 1–7 (2017). http://iopscience.iop.org/article/10.1088/1742-6596/801/1/012047/pdfGoogle Scholar
  34. 34.
    R. Shikder, S. Rahaman, F. Afroze, ABM Alim Al Islam, Keystroke/mouse usage based emotion detection and user identification, in 2017 International Conference on Networking, Systems and Security (NSysS), Dhaka, pp. 96–104, 2017. doi:  https://doi.org/10.1109/NSysS.2017.7885808
  35. 35.
    Z. Changshui, S. Yanhua, AR model for key stroker verification. IEEE Int. Conf. Syst. Man Cybernet. 4, 2887–2890 (2000)Google Scholar
  36. 36.
    W. Eltahir, M. Salami, A. Ismail, W. Lai, Dynamic keystroke analysis using AR model. IEEE Int. Conf. Ind. Technol. 3, 1555–1560 (2004)Google Scholar
  37. 37.
    L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  38. 38.
    N. Bartlow, B. Cukic, Evaluating the reliability of credential hardening through keystroke dynamics, in 2006 17th International Symposium on Software Reliability Engineering, Raleigh, NC, pp. 117–126, 2006. doi:  https://doi.org/10.1109/ISSRE.2006.25
  39. 39.
    A. Salem, D. Zaidan, A. Swidan, R. Saifan, Analysis of strong password using keystroke dynamics authentication in touch screen devices, in 2016 Cybersecurity and Cyberforensics Conference (CCC), Amman, pp. 15–21, 2016. doi:  https://doi.org/10.1109/CCC.2016.11
  40. 40.
    S. Wang, H. Wang, Password authentication using Hopfield neural networks. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(2), 265–268 (2008)CrossRefGoogle Scholar
  41. 41.
    A. Rezaei, S. Mirzakuchaki, A recognition approach using multilayer perceptron and keyboard dynamics patterns, 2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA), Birjand, pp. 1–5, 2013. doi:  https://doi.org/10.1109/PRIA.2013.6528445
  42. 42.
    P.H. Pisani, A.C. Lorena, Negative selection with high-dimensional support for keystroke dynamics, in 2012 Brazilian Symposium on Neural Networks, Curitiba, pp. 19–24, 2012. doi:  https://doi.org/10.1109/SBRN.2012.15
  43. 43.
    S. Yong, W.K. Lai, G. Goghill, Weightless neural networks for typing biometrics authentication, in Knowledge-Based Intelligent Information and Engineering Systems, KES 2004. Lecture Notes in Computer Science, ed. by M.G. Negoita, R.J. Howlett, L.C. Jain, vol. 3214, (Springer, Berlin, Heidelberg, 2004). doi:  https://doi.org/10.1007/978-3-540-30133-2_37Google Scholar
  44. 44.
    S. Mandujano, R. Soto, Deterring password sharing: user authentication via fuzzy c-means clustering applied to keystroke biometric data. in Proceedings of the Fifth Mexican International Conference in Computer Science, pp. 181–187, 2004Google Scholar
  45. 45.
    K. Revett, A bioinformatics based approach to behavioural biometrics, in 2007 Frontiers in the Convergence of Bioscience and Information Technologies, Jeju City, pp. 665–670, 2007. doi:  https://doi.org/10.1109/FBIT.2007.143
  46. 46.
    R. Jugurta, M. Filho, E.O. Freire, On the equalization of keystroke timing histograms. Pattern Recognit. Lett. 27(13), 1440–1446 (2006)CrossRefGoogle Scholar
  47. 47.
    W.G. de Ru, J.H.P. Eloff, Enhanced password authentication through fuzzy logic. IEEE Exp. Intell. Syst. Appl. 12(6), 38–45 (1997)Google Scholar
  48. 48.
    F. Monrose, M.K. Reiter, S. Wetzel, Password hardening based on keystroke dynamics, in Proceedings of the Sixth ACM Conference on Computer and Communications Security, Kent Ridge Digital Labs, Singapore, pp. 73–82, 1999. ISBN: l-58113-148-8Google Scholar
  49. 49.
    R. Giot, M. El-Abed, C. Rosenberger, GREYC keystroke: A benchmark for keystroke dynamics biometric systems, in 2009 I.E. Third International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, pp. 1–6, 2009. doi:  https://doi.org/10.1109/BTAS.2009.5339051
  50. 50.
    R. Giot, M. El-Abed, C. Rosenberger, Web-based benchmark for keystroke dynamics biometric systems: a statistical analysis, in 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Piraeus, pp. 11–15, 2012. doi:  https://doi.org/10.1109/IIH-MSP.2012.10
  51. 51.
    Y. Li, B. Zhang, Y. Cao, S. Zhao, Y. Gao, J. Liu, Study on the BeiHang keystroke dynamics database, in 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, pp. 1–5, 2011. doi:  https://doi.org/10.1109/IJCB.2011.6117485
  52. 52.
    J. Montalvao, C.A.S. Almeida, E.O. Freire, Equalization of keystroke timing histograms for improved identification performance, in 2006 International Telecommunications Symposium, Fortaleza, Ceara, pp. 560–565, 2006. doi:  https://doi.org/10.1109/ITS.2006.4433337
  53. 53.
    K. Killourhy, R. Maxion, Why did my detector do that?!, in International Workshop on Recent Advances in Intrusion Detection, Proceedings of 13th International Symposium, RAID 2010, Ottawa, ON, Canada, 15–17 Sept 2010Google Scholar
  54. 54.
    K.S. Killourhy, R.A. Maxion, Free vs. transcribed text for keystroke-dynamics evaluations, in Proceedings of the 2012 Workshop on Learning from Authoritative Security Experiment Results. ACM, pp. 1–8, 2012. ISBN: 978-1-4503-1195-3, doi:  https://doi.org/10.1145/2379616.2379617
  55. 55.
    J.D. Allen, An analysis of pressure-based keystroke dynamics algorithms, Dissertation. Southern Methodist University, ProQuest Dissertations Publishing, 2010. 1477849Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohammad S. Obaidat
    • 1
  • P. Venkata Krishna
    • 2
    Email author
  • V. Saritha
    • 3
  • Shubham Agarwal
    • 4
  1. 1.Department of Computer and Information ScienceFordham UniversityBronxUSA
  2. 2.Department of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupatiIndia
  3. 3.Department of Computer Science and EngineeringSri Padmavati Mahila VisvavidyalayamTirupatiIndia
  4. 4.Amity School of Engineering and TechnologyAmity UniversityNoidaIndia

Personalised recommendations