International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 4, No. 2, 8 Apr. 2012
Cover page and Table of Contents: PDF (size: 301KB)
Enhanced Password Based Security System Based on User Behavior using Neural Networks
Full Text (PDF, 301KB), PP.29-35
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Author(s)
Index Terms
Artificial neural networks, Keystroke Dynamics, intrusion detection, Security & User Authentication
Abstract
There are multiple numbers of security systems are available to protect your computer/resources. Among them, password based systems are the most commonly used system due to its simplicity, applicability and cost effectiveness But these types of systems have higher sensitivity to cyber-attack. Most of the advanced methods for authentication based on password security encrypt the contents of password before storing or transmitting in the physical domain. But all conventional encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency.
In this paper an enhanced password based security system has been proposed based on user typing behavior, which will attempt to identify authenticity of any user failing to login in first few attempts by analyzing the basic user behaviors/activities and finally training them through neural network and classifying them as genuine or intruder.
Cite This Paper
Preet Inder Singh, Gour Sundar Mitra Thakur, "Enhanced Password Based Security System Based on User Behavior using Neural Networks", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.4, no.2, pp.29-35, 2012. DOI:10.5815/ijieeb.2012.02.05
Reference
[1]D. Shanmugapriya and G. Padmavathi, "A Survey of Biometric keystroke Dynamics: Approaches, Security and Challenges", (IJCSIS) International Journal of Computer Science and Information Security, Vol. 5, No. 1, 2009.
[2]R. Giot, M. El-Abed, C. Rosenberger, "Keystroke Dynamics Authentication for Collaborative Systems" 2009.
[3]A. Peacock, X. Ke and M. Wilkerson, "Typing patterns: A key to user Identification", IEEE Security and Privacy 2(5) (2004).
[4]L. O'Gorman, "Comparing Passwords, Tokens, and Biometrics for User Authentication", Proceedings of the IEEE, Vol. 91, No. 12, Dec, pp. 2019-2040, 2003.
[5]A. K. Jain, A. Ross and S. Prabhakar, "An Introduction to Biometric Recognition", IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, Vol. 14, No. 1, January 2004.
[6]A. Awad, E. Ahmed, and I. Traore, "Anomaly Intrusion Detection based on Biometrics", Proceedings of the IEEE, 2005.
[7]M. K. Singh, "Password Based a Generalize Robust Security System design using Neural Network", IJCSI International Journal of Computer Science Issues, Vol. 4, No. 2, 2009
[8]N. Harun, W. L. Woo and S. S. Dlay, "Performance of Keystroke Biometrics Authentication System Using Artificial Neural Network (ANN) and Distance Classifier Method", International Conference on Computer and Communication Engineering (ICCCE 2010), 11-13 May 2010, Kuala Lumpur, Malaysia.
[9]Araujo, L.C.F., Sucupira, L.H.R., Lizarraga, M.G., Ling, L.L., Yabu-Uti, J.B.T., "User authentication through typing biometrics features", IEEE Trans. on Signal Processing, 53 (2), 851–855, (2005).