Continuous authentication for the user based on Keystroke Dynamics (Applied to commonly and rarely used Arabic words)
Abstract
Continuous Authentication considered as one of the authentication methods that has been emerged to solve the problem verifying whether who is using the system is the same one from the moment of login to the moment of logout, Keystroke Dynamics is one of the continuous authentication methods that can be used for authentication either in the form of fixed text or free text. The purpose of this paper is to study the possibility of using specific Arabic words to authenticate the user continuous authentication based on Keystroke Dynamics, as well as to examine the differences between the performance of the authentication system when using the commonly used words in authentication and its performance when using rarely used words in authentication. In this study, one of the continuous authentication algorithms based on Keystroke Dynamics has been applied to ten Arabic words of commonly used words in e-mail, and three Arabic words which are rarely used, where four of the timing characteristics were used to extract the features of the typing style of users on the keyboard. To classify user's data, a measure of distance from the statistical approach, which is the Scaled Manhattan distance measure, Individual threshold, trust level and Tlockout were used. The results have shown that it is possible to authenticate users using continuous authentication based on the Arabic Language Keystroke Dynamics of fixed text .
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References
[1] Bours, P., & Barghouthi, H. (2009). Continuous Authentication using Biometric Keystroke Dynamics. The Norwegian Information Security Conference (NISK 2009).
[2] Alshehri, A., Coenen, F., & Bollegala, D. (2018). Iterative Keystroke Continuous Authentication: A Time Series Based Approach. KI – Künstliche Intelligenz, 32, 231–243.
[3] El Menshawy, D., Mokhtar, H., & Hegazy, O. (2014). A Keystroke Dynamics Based Approach for Continuous Authentication. Springer International Publishing Switzerland.
[4] Patil, R. A., & Renke, A. L. (2016). Keystroke Dynamics for User Authentication and Identification by Using Typing Rhythm. International Journal of Computer Applications, 144(9), June 2016.
[5] Rundhaug, F. (2007). Keystroke Dynamics: Can Attackers Learn Someone’s Typing Characteristics? Master’s Thesis in Information Security, Department of Computer Science and Media Technology, Gjøvik University College.
[6] Alsolami, E. (2012). An Examination of Keystroke Dynamics for Continuous User Authentication. PhD Thesis, Information Security Institute, Science and Engineering Faculty, Queensland University of Technology.
[7] Giot, R., El-Abed, M., & Rosenberger, C. Fast computation of the performance evaluation of biometric systems: application to multibiometric. Future Generation Computer Systems (FGCS).
[8] Oliveira, T. (2014). Improving Web Authentication with Keystroke Dynamics. Master’s Thesis in Computer Engineering, IT Department, University of Minho.
[9] Teh, P. S., Teoh, A. J., & Yue, S. (2013). A Survey of Keystroke Dynamics Biometrics. The Scientific World Journal, 2013, Article ID 408280, 24 pages.
[10] Srinivasa, K. G., & Gosukonda, S. (2014). Continuous multimodal user authentication: coupling hard and soft biometrics with support vector machines to attenuate noise. CSIT 2014.
[11] Traoré, I., & Awad, A. (2012). Continuous Authentication Using Biometrics: Data, Models, and Metrics. Information Science Reference, IGI Global, United States of America.
[12] Ness, J. (2017). Presentation Attack and Detection in Keystroke Dynamics. Master’s Thesis in Information Security, Department of Information Security and Communication Technology, Norwegian University of Science and Technology.
[13] Teh, P., Teoh, A. J., & Yue, S. (2013). A Survey of Keystroke Dynamics Biometrics. Hindawi Publishing Corporation, The Scientific World Journal.
[14] Malik, J., & Girdhar, D. (2014). Reference Threshold Calculation for Biometric Authentication. International Journal of Image, Graphics and Signal Processing, 2014(2), 46–53.
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