User Authentication and Identification Using Neural Network

Md Liakat Ali*, Kutub Thakur**, Charles C. Tappert***
*-** Ph.D Scholar, Department of Computer Science and Engineering, Pace University, Newyork.
*** Professor, Seidenberg School of CSIS, Newyork.


Now-a-days people are heavily dependent on computers to store and process important information. User authentication and identification has become one of the most important and challenging issue in order to secure them from intruders. As traditional user ID and password scheme have failed to provide information security, keystroke dynamics authentication systems can be used to strengthen the existing security techniques. Keystroke dynamic authentication systems are transparent, low cost, and non-invasive for the user, but it has lower accuracy and lower performance compared to other biometric authentication systems. The aim of this paper is to depict a detailed survey of the researches on keystroke dynamic authentication that have used neural networks for classification described in the last two decades. The summary, accuracy of each experiment, and shortcomings of those researches have been presented in this study. Finally, the paper addresses some challenges in keystroke dynamic authentication systems using neural networks that need to be resolve in order to get better performance.


Authentication, Identification, keystroke Dynamic, Neural Network, Comparison.

How to Cite this Article?

Ali, M. L., Thakur, K., and Tappert, C. C. (2015).User Authentication and Identification Using Neural Network. i-manager’s Journal on Pattern Recognition, 2(2), 34-45.


[1]. Ahmad, A. M., & Abdullah, N. N. (2000). “User authentication via neural network”, Lecture Notes in Computer Science, pp. 310–320.
[2]. Ahmed, A. A. E., Traore,´ I., & Ahmed, A. (2008). “Digital fingerprinting based on keystroke dynamics”, In Haisa, pp. 94 -104.
[3]. Ali, H., Salami, M. J., et al. (2009). “Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers”, In Signal Processing and its Applications, CSPA 2009, 5th International Colloquium, pp. 198-203.
[4]. Ali, M. L., Monaco, J. V., & Tappert, C. C. (2015). “Hidden markov models in keystroke dynamics”, Proceedings of Student-Faculty Research Day, New York, USA: Pace University.
[5]. Alsultan, A., & Warwick, K. (2013). “Keystroke dynamics authentication: a survey of free-text methods”, International Journal of Computer Science, Vol.10(4), pp.1–10.
[6]. Antal, M., Szabo,´ L. Z., & Laszl´o,´ I. (2015). “Keystroke dynamics on android platform”, Procedia Technology, Vol.19, pp. 820–826.
[7]. Banerjee, S. P., & Woodard, D. L. (2012). “Biometric authentication and identification using keystroke dynamics: A survey”, Journal of Pattern Recognition Research, Vol. 7(1), pp.116–139.
[8]. Bechtel, J., Serpen, G., & Brown, M. (2002). “Passphrase authentication based on typing style through an art 2 neural network”, International Journal of Computational Intelligence and Applications, Vol. 2(02), pp. 131–152.
[9]. Bleha, S. A., & Obaidat, M. S. (1993). “Computer users verification using the perceptron algorithm”. Systems, IEEE Transaction on Man and Cybernetics, Vol. 23(3), pp. 900- 902.
[10]. Brown, M., & Rogers, S. J. (1993). “User identification via keystroke characteristics of typed names using neural networks”, International Journal of Man-Machine Studies, Vol. 39(6), pp. 999–1014.
[11]. Capuano, N., Marsella, M., Miranda, S., & Salerno, S. (1999). User authentication with neural networks. Univerity of Salerno Italy ”, http://www. capuano. biz/Papers/EANN 99. pdf .
[12]. Cho, S., Han, C., Han, D. H., & Kim, H.-I. (2000). “Webbased keystroke dynamics identity verification using neural network”, Journal of Organizational Computing and Electronic Commerce, Vol.10(4), pp. 295–307.
[13]. Clarke, N. L., & Furnell, S. (2007). “Authenticating mobile phone users using keystroke analysis”, International Journal of Information Security, Vol. 6(1), pp. 1–14.
[14]. Crawford, H. (2010). “Keystroke dynamics: Characteristics and opportunities”, In Privacy Security and Trust (PST), Eighth Annual International Conference, pp. 205–212.
[15]. Dowland, P. S., & Furnell, S. M. (2004). “A long-term trial of keystroke profiling using digraph, trigraph and keyword latencies”, In Security and Protection in Information Processing Systems, pp. 275–289, Springer.
[16]. Furnell, S., Morrissey, J. P., Sanders, P. W., & Stockel, C. T. (1996). “Applications of keystroke analysis for improved login security and continuous user authentication”, In Information Systems Security, pp. 283-294.
[17]. Harun, N., Dlay, S. S., & Woo, W. L. (2010). “Performance of keystroke biometrics authentication system using Multilayer Perceptron Neural Network (MLPNN)”, In Communication Systems Networks and Digital Signal Processing CSNDSP, 7th International Symposium, pp. 711-714.
[18]. Harun, N., Woo, W. L., & Dlay, S. S. (2010). “Performance of keystroke biometrics authentication system using Artificial Neural Network (ANN) and distance classifier method”, In Computer and Communication Engineering (ICCCE) International Conference, pp. 1–6.
[19]. Joyce, R., & Gupta, G. (1990). “Identity authentication based on keystroke latencies”, Communications of the ACM, Vol. 33(2), pp.168–176.
[20]. Karnan, M., & Akila, M. (2010). “Personal authentication based on keystroke dynamics using soft computing techniques”, In Communication Software and Networks, ICCSN'10. second international conference, pp. 334-338.
[21]. Kohonen, T. (1982). “Self-organized formation of topologically correct feature maps”, Biological Cybernetics, Vol. 43(1), pp. 59- 69.
[22]. Krenker, A., Kos, A., & Bester,? J. (2011). Introduction to the Artificial Neural Networks. INTECH Open Access Publisher.
[23]. Lammers, A., & Langenfeld, S. (1991).Identity authentication based on keystroke latencies using neural networks”, Journal of Computing Sciences in Colleges, Vol. 6(5), pp. 48– 51.
[24]. Lin, D.-T. (1997). “Computer-access authentication with neural network based keystroke identity verification”, In Proceedings of IEEE International Conference on Neural Networks, Vol. 1, pp. 174 -178.
[25]. Loy, C. C., Lai, W., & Lim, C. (2005). “Development of a pressure-based typing biometrics user authentication system”, ASEAN Virtual Instrumentation Applications Contest Submission.
[26]. Loy, C. C., Lai, W. K., & Lim, C. P. (2007). “Keystroke patterns classification using the artmap-fd neural network”, In Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2007. Third International Conference, Vol. 1, pp. 61- 64.
[27]. Maisuria, L. K., Ong, C. S., & Lai, W. K. (1999). “A comparison of artificial neural networks and cluster analysis for typing biometrics authentication”, In Neural Networks, IJCNN'99, International Joint Conference, Vol. 5, pp. 3295-3299.
[28]. Mantyj¨arvi,¨ J., Koivumaki,¨ J., & Vuori, P. (2002). “Keystroke recognition for virtual keyboard”, In Multimedia and Expo, ICME'02. Proceedings of IEEE International Conference, Vol. 2, pp. 429-432.
[29]. Nguyen, T. T., Le, T. H., & Le, B. H. (2010). “Keystroke dynamics extraction by independent component analysis and bio-matrix for user authentication”. In Pricai 2010: Trends in Artificial Intelligence, pp. 477-486. Springer.
[30]. Nikelshpur, D. O. (2014). Achieving consistent nearoptimal pattern recognition accuracy using particle swarm opti-mization to pre-train artificial neural networks (Unpub-lished doctoral dissertation), Pace University.
[31]. Obaidat, M., & Macchairolo, D. (1994). “A multilayer neural network system for computer access security”, Systems, Man and Cybernetics, IEEE Transactions, Vol. 24(5), pp. 806-813.
[32]. Obaidat, M. S. (1995). “Verification methodology for computer systems users”, In Proceedings of the 1995 ACM Symposium on Applied Computing, pp. 258-262.
[33]. Obaidat, M. S., & Sadoun, B. (1997). “Verification of computer users using keystroke dynamics”, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions, Vol. 27(2), pp. 261-269.
[34]. Pavaday, N., & Soyjaudah, K. (2007). “Investigating performance of neural networks in authentication using keystroke dynamics, In Africon 2007.
[35]. Peacock, A., Ke, X., & Wilkerson, M. (2004). “Typing patterns: A key to user identification”, IEEE Security & Privacy, Vol. 5, pp. 40–47.
[36]. Revett, K., de Magalhes, S. T., & Santos, H. M. D. (2007). “On the use of rough sets for user authen-tication via keystroke dynamics”, In Proceedings of the Portuguese Conference on Artificial Intelligence (epia 07), pp. 145-159, Guimares, Portugal.
[37]. Saevanee, H., & Bhattarakosol, P. (2009). “Authenticating user using keystroke dynamics and finger pressure”, In 6th IEEE Consumer Communications and Networking Conference (CCNC09), pp. 1-2, Las Vegas, NV: IEEE. doi: 10.1109/CCNC.2009.4784783
[38]. Shanmugapriya, D., & Padmavathi, G. (2009). A survey of biometric keystroke dynamics: Approaches, security and challenges. arXiv preprint arXiv:0910.0817.
[39]. Sulong, A., Siddiqi, M., et al. (2009). “Intelligent key stroke pressure - based typing biometrics authentication system using radial basis function network”, In Signal Processing and its Applications, CSPA 2009. 5th International Colloquium, pp. 151-155.
[40]. Teh, P. S., Teoh, A. B. J., & Yue, S. (2013). “A survey of keystroke dynamics biometrics”, The Scientific World Journal.
[41]. Wong, F. W. M. H., Supian, A. S. M., Ismail, A. F., Kin, L. W., & Soon, O. C. (2001). “Enhanced user authentication through typing biometrics with artificial neural networks and k-nearest neighbor algorithm”, In Signals, Systems and Computers, Conference Record of the thirty-fifth Asilomar Conference, Vol. 2, pp. 911-915.
[42]. Yong, S., Lai, W. K., & Goghill, G. (2004). “Weightless neural networks for typing biometrics authentication”, In Knowledge-based Intelligent Information and Engineering Systems, pp. 284-293.
[43]. Yu, E., & Cho, S. (2003). “Novelty detection approach for keystroke dynamics identity verification”, In Intelligent Data Engineering and Automated Learning, pp. 1016-1023, Springer.
[44]. [Zhong, Y., & Deng, Y. (2015). A survey on keystroke dynam-ics biometrics: Approaches, advances, and evaluations, Vol. 2, Science Gate Publishing.
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