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.
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.
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
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
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
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
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.
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
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
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
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
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.
