Abstract
Keystroke dynamics-based authentication (KDA) is to verify a user’s identification using not only the password but also keystroke patterns. The authors have shown in previous research that uniqueness and consistency of keystroke patterns are important factors to authentication accuracy and that they can be improved by employing artificial rhythms and tempo cues. In this paper, we implement the pause strategy and/or auditory cues for KDA and assess their effectiveness using various novelty detectors. Experimental results show that improved uniqueness and consistency lead to enhanced authentication performance, in particular for those users with poor typing ability.
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Gaines, R., Lisowski, W., Press, S., Shapiro, N.: Authentication by keystroke timing: some preliminary results. Rand Report R-256-NSF. Rand Corporation (1980)
Umphress, D., Williams, G.: Identity Verification through Keyboard Characteristics. International Journal of Man-Machine Studies 23, 263–273 (1985)
Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer, Norwell (1999)
Polemi, D.: Biometric Techniques: Review and Evaluation of Biometric Techniques for Identification and Authentication. Technical Report. Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece (1997), Available at: ftp://ftp.cordis.lu/pub/infosec/docs/biomet.doc
Monrose, F., Rubin, A.D.: Keystroke Dynamics as a Biometric for Authentication. Future Generation Computer System 16(4), 351–359 (2000)
Cho, S., Han, C., Han, D., Kim, H.: Web Based Keystroke Dynamics Identity Verification Using Neural Networks. Journal of Organizational Computing and Electronic Commerce 10(4), 295–307 (2000)
Yu, E., Cho, S.: Keystroke Dynamics Identity Verification - Its Problems and Practical Solutions. Computer and Security 23(5), 428–440 (2004)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the Support of a High-dimensional Distribution. Neural Computation 13, 1443–1471 (2001)
Cho, S., Hwang, S.: Artificial Rhythms and Cues for Keystroke Dynamics-based Authentication. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 626–632. Springer, Heidelberg (2005)
Kang, P., Park, S., Cho, S., Hwang, S., Lee, H.: The Effectiveness of Artificial Rhythms and Cues for Keystroke Dynamics-based User Authentication (submitted, 2006)
Barnett, V., Lewis, T.: Outliers in Statistical Data. Wiley and Sons, New York (1994)
Bishop, C.: Novelty Detection and Neural Network Validation. In: Proceedings of IEE Conference on Vision and Image Signal Processing, pp. 217–222 (1994)
Knorr, E., Ng, R., Tucakov, V.: Distance-based Outliers: Algorithms and Applications. VLDB Journal 8(3), 237–253 (2000)
Lee, H., Cho, S.: SOM-based Novelty Detection Using Novel Data. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 359–366. Springer, Heidelberg (2005)
Golarelli, M., Maio, D., Maltoni, D.: On the Error-Reject Trade-off in Biometric Verification Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 786–796 (1997)
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Hwang, Ss., Lee, Hj., Cho, S. (2006). Improving Authentication Accuracy of Unfamiliar Passwords with Pauses and Cues for Keystroke Dynamics-Based Authentication. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_9
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DOI: https://doi.org/10.1007/11734628_9
Publisher Name: Springer, Berlin, Heidelberg
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