Abstract
Classification of users’ keystroke patterns captured from a typing biometrics system is discussed in this paper. Although the user identification system developed here requires the user to key-in their passwords as they would normally do, the identification of the users will only be based on their keystroke patterns rather than the actual passwords. The keystroke pattern generated is represented by the force applied on a numerical keypad and it is this set of features extracted from a common password that will be submitted to the classifiers to identify the different users. The typing biometrics system had been designed and developed with an 8-bit microcontroller that is based on the AVR enhanced RISC architecture. Classification of these keystroke patterns will be with PSO (particle swarm optimization) and this will be compared with the standard K-Means. The preliminary experimental results showed that the identity of users can be authenticated based solely on their keystroke biometric patterns from a numeric keypad.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Joyce, R., Gupta, G.: Identity authentication based on keystroke latencies. Comm. ACM 33, 168–176 (1990)
Monrose, F., Rubin, A.: Authentication via keystroke dynamics. In: Proceedings of the Fourth ACM Conference on Computer and Communications Security, pp. 48–56, Zurich, Switzerland (1997)
De Ru, W., Eloff, J.: Enhanced password authentication through fuzzy logic. IEEE Expert 12, 38–45 (1997)
Obaidat, M.S., Sadoun, B.: Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. 27, 261–269 (1997)
Tee, E.R., Selvanathan, N.: Pin signature verification using wavelet transform. Malays. J. Comput. Sci. 9(2), 71–78 (1996)
Maisuria, L.K., Ong, C.S., Lai, W.K.: A comparison of artificial neural networks and cluster analysis for typing biometrics authentication. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Washington, DC, 10–16 July 1999
Loy, C.C., Lim, C.P., Lai, W.K.: Pressure-based typing biometrics user authentication using the fuzzy ARTMAP neural network. In: Proceedings of the Twelfth International Conference on Neural Information Processing (ICONIP 2005), pp 647–652, Taipei, Taiwan ROC, October 30–November 2, 2005
Eltahir, W.E., Salami, M.J.E., Ismail, A.F., Lai, W.K.: Design and evaluation of a pressure-based typing biometric authentication system. EURASIP J. Inf. Sec. 2008 14 (2008). Article ID 345047 doi:10.1155/2008/345047
Yong, S, Lai, W.K., Coghill, G.: Weightless neural networks for typing biometrics authentication. In: Proceedings of the 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004, vol. II, pp. 284–293, Wellington, New Zealand, 22–24 September 2004
Obaidat, M.S., Sadoun, B.: Keystroke dynamics based authentication (Chap. 10). In: Jain, A.K., Bolle, R., Pankanti, S. (eds.) Biometrics: Personal Identification in Networked Society. Springer, US (1996)
A global revolution is taking place and YALE Digital Door Lock. http://www.yalelock.com/en/yale/com/Global-Revolution/. Accessed 22 March 2015
Lock Bumping. http://e.wikipedia.org/wiki/Lock_bumping. Accessed 22 March 2015
Force Sensing resistor. http://en.wikipedia.org/wiki/Force-sensing_resistor. Accessed 18th March 2015
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kiran, M., Teng, S.L., Seng, C.C., Kin, L.W.: Human posture classification using hybrid particle swarm optimization. In: Proceedings of the Tenth International Conference on Information Sciences, Signal Processing and their application (ISSPA 2010), Kuala Lumpur, Malaysia 10–13 May 2010
Omran, M.G.H.: Particle swarm optimization methods for pattern recognition and image processing. Ph.D. thesis, University of Pretoria (2005)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: The Fifth Berkeley Symposium on Mathematics, Statistics and Probability, pp. 281–297. University of California Press (1967)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651–666 (2010)
Lai, W.K., Tan, B.G., Soo, M.S., Khan, I.: Two-factor user authentication with the CogRAM weightless neural net. In: Proceedings of the 2014 World Congress on Computational Intelligence (WCCI 2014), Beijing, China, 6–11 July 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lai, W.K., Tan, B.G., Soo, M.S., Khan, I. (2015). Classification of Keystroke Patterns for User Identification in a Pressure-Based Typing Biometrics System with Particle Swarm Optimization (PSO). In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-26532-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26531-5
Online ISBN: 978-3-319-26532-2
eBook Packages: Computer ScienceComputer Science (R0)