All Title Author
Keywords Abstract


Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

DOI: 10.4236/jmp.2018.92008, PP. 112-129

Keywords: Keystroke Dynamics, Information Set, Renyi Entropy Function and Its Possibilistic Version, Composite Fuzzy Classifier

Full-Text   Cite this paper   Add to My Lib

Abstract:

This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.

References

[1]  Loy, C.C., Lim, C.P. and Lai, W.K. (2005) Pressure-Based Typing Biometrics User Authentication Using the Fuzzy ARTMAP Neural Network. Proceedings of the Twelfth International Conference on Neural Information Processing (ICONIP 2005), Taiwan, 30 October-2 November 2005, 647-652.
[2]  Loy, C.C., Lai, W.K. and Lim, C.P. (2007) Keystroke Patterns Classification Using the ARTMAP-FD Neural Network. Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kaohsiung, 26-28 November 2007, Vol. 1, 61-64.
https://doi.org/10.1109/IIH-MSP.2007.218
[3]  Killourhy, K.S. and Maxion, R.A. (2009) Comparing Anomaly-Detection Algorithms for Keystroke Dynamics. IEEE/IFIP International Conference on Dependable Systems & Networks, Lisbon, 29 June-2 July 2009, 125-134.
https://doi.org/10.1109/DSN.2009.5270346
[4]  Giot, R., El-Abed, M. and Rosenberger, C. (2009) GREYC Keystroke: A Benchmark for Keystroke Dynamics Biometric Systems. IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, Washington DC, 28-30 September 2009, 1-6.
https://doi.org/10.1109/BTAS.2009.5339051
[5]  Montalvão Filho, J.R. and Freire, E.O. (2006) Pattern Recognition Letters, 27, 1440-1446.
https://doi.org/10.1016/j.patrec.2006.01.010
[6]  Vural, E., Huang, J., Hou, D. and Schuckers, S. (2014) Shared Research Dataset to Support Development of Keystroke Authentication, IEEE International Joint Conference on Biometrics (IJCB), Clearwater, 29 September-2 October 2014, 1-8.
https://doi.org/10.1109/BTAS.2014.6996259
[7]  Wangsuk, K. and Anusas-Amornkul, T. (2013) Procedia Computer Science, 24, 175-183.
https://doi.org/10.1016/j.procs.2013.10.041
[8]  Hosseinzadeh, D. and Krishnan, S. (2008) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38, 816-826.
[9]  Eker, H. and Upadhyaya, S. (2015) Enhanced Recognition of Keystroke Dynamics Using Gaussian Mixture Models. IEEE Military Communications Conference, Tampa, 26-28 October 2015, 1305-1310.
[10]  Deng, Y. and Zhong, Y. (2013) ISRN Signal Processing, 2013, Article ID: 565183.
[11]  The, P.S., Teoh, A.B.J., Ong, T.S. and Neo, H.F. (2007) Statistical Fusion Approach on Keystroke Dynamics. Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, Shanghai, 16-18 December 2007, 918-923.
https://doi.org/10.1109/SITIS.2007.46
[12]  Thanganayagam, R. and Thangadurai, A. (2016) Hybrid Model with Fusion Approach to Enhance the Efficiency of Keystroke Dynamics Authentication. Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, 85-96.
https://doi.org/10.1007/978-81-322-2538-6_10
[13]  Pisani, P.H., Giot, R., De Carvalho, A.C. and Lorena, A.C. (2016) Computers & Security, 60, 134-153.
https://doi.org/10.1016/j.cose.2016.04.004
[14]  Ivannikova, E., David, G. and Hämäläinen, T. (2017) Anomaly Detection Approach to Keystroke Dynamics Based User Authentication. IEEE Symposium on Computers and Communications (ISCC), Heraklion, 3-6 July 2017, 885-889.
https://doi.org/10.1109/ISCC.2017.8024638
[15]  Mhenni, A., Rosenberger, C., Cherrier, E. and Ben Amara, N.E. (2016) Keystroke Template Update with Adapted Thresholds. 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Monastir, 21-23 March 2016, 483-488.
https://doi.org/10.1109/ATSIP.2016.7523122
[16]  Bhatia, A. and Hanmandlu, M. (2017) Journal of Modern Physics, 8, 1557-1583.
https://doi.org/10.4236/jmp.2017.89094
[17]  Hanmandlu, M., et al. (2014) Engineering Applications of Artificial Intelligence, 36, 269-286.
https://doi.org/10.1016/j.engappai.2014.06.028
[18]  Pal, N.R. and Pal, S.K. (1991) IEEE Transactions on Systems, Man, and Cybernetics, 21, 1260-1270.
https://doi.org/10.1109/21.120079
[19]  Rényi, A., et al. (1961) On Measures of Entropy and Information. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, University of California Press, Berkeley, California, 20 June-30 July 1960, 547-561.
[20]  Hanmandlu, M. and Das, A. (2011) Defence Science Journal, 61, 415-430.
https://doi.org/10.14429/dsj.61.1177
[21]  Arora, P., Hanmandlu, M. and Srivastava, S. (2015) Pattern Recognition Letters, 68, 336-342.
https://doi.org/10.1016/j.patrec.2015.05.016
[22]  Sayeed, F. and Hanmandlu, M. (2017) Knowledge and Information Systems, 52, 485-507.
https://doi.org/10.1007/s10115-016-1017-x
[23]  Mamta and Hanmandlu, M. (2013) Expert Systems with Applications, 40, 6478-6490.
https://doi.org/10.1016/j.eswa.2013.05.020

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal