Abstract
Keystroke dynamics is a method of discriminating users based on their typing patterns, and can be used as a second factor in uniquely identifying users when they type their passwords. Most studies that have been performed in this domain focus on devising new techniques to design more accurate systems, but little attention has been given to make them practically feasible in real-life applications. A key limitation is the scarce availability of training samples from a user.
We have designed a framework that accepts only 10 training samples from a user, and synthesises 190 more training samples from these using Synthetic Minority Oversampling Technique (SMOTE). The accuracy of this framework is comparable to those frameworks that use a large number of training samples collected from a user and are not practical in real-life applications. We used the standard and freely available CMU dataset and their evaluation methodologies as a benchmark.
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References
Aloul, F., Zahidi, S., El-Hajj, W.: Multi factor authentication using mobile phones. Int. J. Math. Comput. Sci. 4(2), 65–80 (2009)
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)
Jain, A.K., Dass, S.C., Nandakumar, K.: Soft biometric traits for personal recognition systems. In: Biometric Authentication, pp. 731–738. Springer (2004)
Rubin, D.: Keystroke dynamics as a biometric for authentication. Future Gener. Comput. Syst. 16(4), 351–359 (2000)
Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: 2009 IEEE/IFIP International Conference on Dependable Systems & Networks, pp. 125–134. IEEE (2009)
Giot, R., El-Abed, M., Rosenberger, C.: Greyc keystroke: a benchmark for keystroke dynamics biometric systems. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS 2009, pp. 1–6. IEEE (2009)
Cho, S., Han, C., Han, D.H., Kim, H.I.: Web-based keystroke dynamics identity verification using neural network. J. Organ. Comput. Electron. Commer. 10(4), 295–307 (2000)
Yu, E., Cho, S.: GA-SVM wrapper approach for feature subset selection in keystroke dynamics identity verification. In: Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 2253–2257. IEEE (2003)
Bleha, S., Slivinsky, C., Hussien, B.: Computer-access security systems using keystroke dynamics. IEEE Trans. Pattern Anal. Mach. Intell. 12(12), 1217–1222 (1990)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)
Araujo, L.C.F., Sucupira, J.L.H.R., Lizarraga, M.G., Ling, L.L., Yabu-uti, J.B.T.: User authentication through typing biometrics features. In: Biometric Authentication, pp. 694–700. Springer (2004)
Haider, S., Abbas, A., Zaidi, A.K.: A multi-technique approach for user identification through keystroke dynamics. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1336–1341. IEEE (2000)
CENELEC. European standard en 50133-1: Alarm systems. access control systems for use in security applications. part 1: System requirements. Standard Number EN 50133-1:1996/A1:2002, Technical Body CLC/TC 79, European Committee for Electrotechnical Standardization (CENELEC) (2002)
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Payal, S., Garware, B., Kelkar, S. (2018). Towards Designing a Framework for Practical Keystroke Dynamics Based Authentication. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_24
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DOI: https://doi.org/10.1007/978-3-319-60618-7_24
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