Abstract
Securing the sensitive data and computer systems by allowing ease access to authenticated users and withstanding the attacks of imposters is one of the major challenges in the field of computer security. Traditionally, ID and password schemes are most widely used for controlling the access to computer systems. But, this scheme has many flaws such as Password sharing, Shoulder surfing, Brute force attack, Dictionary attack, Guessing, Phishing and many more. Biometrics technologies provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working with a keyboard. In this paper, neural network approaches with three different passwords namely weak, medium and strong passwords are taken into consideration and accuracy obtained is compared.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ahmed, A.A.E., Traore, I.: Anomaly Intrusion Detection based on Biometrics. In: Proceedings of 6th IEEE Information Assurance Workshop, pp. 452–453 (2005)
Jain, A., NandaKumar, K., Ross, A.: Score normalization in multimodal biometric systems. IEEE Pattern Recognition 38, 2270–2285 (2005)
Bergando, et al.: User Authentication through keystroke Dynamics. ACM transaction on Information System Security (5), 367–397 (2002)
Brown, M., Rogers, J.: User Identification via Keystroke Characteristics of Typed Names using Neural Networks. International Journal of Man-Machine Studies 39, 999–1014 (1993)
Cho, et al.: Web based keystroke dynamics identity verification using neural network. Journal of organizational computing and electronic commerce 10(4), 295–307 (2000 )
Clarke, N.L., Furnell, S.M.: Authenticating mobile phone users using keystroke analysis. International Journal of Information Security 6(1), 1–14 (2007)
Yu, E., Cho, S.: Keystroke dynamics identity verification and its problems and practical solutions. Computers & Security (2004)
Gunetti, Picardi,: Keystroke analysis of free text. ACM Transactions on Information and System Security, 312–347 (2005)
http://www.microsoft.com/protect/yourself/password/checker.mspx
Lee, H., Cho, S.: Retraining a keystroke dynamics-based authenticator with impostor patterns. Computers & Security 26(4), 300–310 (2007)
O’Gorman, L.: Comparing Passwords, Tokens, and Biometrics for User Authentication. Proceedings of the IEEE, 2019–2040 (2003)
Monrose, F., Reiter, M., Wetzel, S.: Password Hardening Based on Keystroke Dynamics. IIJS, 1–15 (2001)
Monrose, F., Rubin, A.: Authentication via Keystroke Dynamics. In: Proceedings of the 4th ACM Conference on Computer and Communications Security, pp. 48–56 (1997)
Monrose, R., Rubin, A.: Keystroke Dynamics as a Biometric for Authentication. Future Generation Computer Systems, 351–359 (1999)
Obaidat, M.S., Sadoun, B.: Verification of Computer User Using Keystroke Dynamics. IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics 27(2) (1997)
Ord, T., Furnell, S.: User Authentication for Keypad-Based Devices using Keystroke Analysis, MSc Thesis, University of Plymouth, UK (2000)
Bleha, S., Obaidat, M.S.: Computer user verification using the perceptron. IEEE Trans. Systems, Man, and Cybernetics 23(3), 900–902 (1993)
Hwang, S.-S., Cho, S., Park, S.: Keystroke dynamics based authentication for mobile phones. Computers & Securit, 85–93 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shanmugapriya, V., Padmavathi, G. (2010). Keystroke Dynamics Authentication Using Neural Network Approaches. In: Das, V.V., Vijaykumar, R. (eds) Information and Communication Technologies. ICT 2010. Communications in Computer and Information Science, vol 101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15766-0_121
Download citation
DOI: https://doi.org/10.1007/978-3-642-15766-0_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15765-3
Online ISBN: 978-3-642-15766-0
eBook Packages: Computer ScienceComputer Science (R0)