Abstract
In this paper we investigate the problem of user authentication based on keystroke timing pattern. We propose a simple, robust and non parameterized nearest neighbor regression based feature ranking algorithm for anomaly detection. Our approach successfully handle drawbacks like outlier detection, scale variation and prevents overfitting. Apart from using existing keystroke timing features from the dataset like dwell time and flight time, other features namely bigram time and inversion ratio time are engineered as well. The efficiency and effectiveness of our method is demonstrated through extensive comparisons with other state-of-the-art techniques using CMU keystroke dynamics benchmark dataset and has shown great results in terms of average equal error rate (EER) than other proposed techniques. We achieved an average equal error rate of 0.051 for the user authentication task.
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References
Dvorak, A., Merrick, N., Dealey, W., Ford, G.: Typewriting behavior (1936)
Al-Jarrah, M.M.: An anomaly detector for keystroke dynamics based on medians vector proximity. J. Emerg. Trends Comput. Inf. Sci. 3(6), 988–993 (2012)
Bergadano, F., Gunetti, D., Picardi, C.: User authentication through keystroke dynamics. ACM Trans. Inf. Syst. Secur. (TISSEC) 5(4), 367–397 (2002)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Forsen, G.E., Nelson, M.R., Staron Jr., R.J.: Personal attributes authentication techniques. Technical report, DTIC Document (1977)
Gaines, R.S., Lisowski, W., Press, S.J., Shapiro, N.: Authentication by keystroke timing: some preliminary results. Technical report, DTIC Document (1980)
Giot, R., Hemery, B., Rosenberger, C.: Low cost and usable multimodal biometric system based on keystroke dynamics and 2D face recognition. In: ICPR (2010)
Gunetti, D., Picardi, C.: Keystroke analysis of free text. ACM Trans. Inf. Syst. Secur. (TISSEC) 8(3), 312–347 (2005)
Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, IJCNN, vol. 1, pp. 593–605 (1989)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: DSN (2009)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). CoRR abs/1412.6980
Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Ng, A.Y.: On optimization methods for deep learning. In: ICML (2011)
Leggett, J., Williams, G.: Verifying identity via keystroke characterstics. Int. J. Man Mach. Stud. 28(1), 67–76 (1988)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models (2013)
Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication. Future Gener. Comput. Syst. 16(4), 351–359 (2000)
Peacock, A., Ke, X., Wilkerson, M.: Typing patterns: a key to user identification. IEEE Secur. Priv. 2(5), 40–47 (2004)
Schapire, R.E.: A brief introduction to boosting. In: IJCAI (1999)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Syed, Z., Banerjee, S., Cheng, Q., Cukic, B.: Effects of user habituation in keystroke dynamics on password security policy. In: HASE (2011)
Syed, Z., Banerjee, S., Cukic, B.: Leveraging variations in event sequences in keystroke-dynamics authentication systems. In: HASE (2014)
Zhong, Y., Deng, Y., Jain, A.K.: Keystroke dynamics for user authentication. In: CVPR (2012)
Acknowledgement
This work was supported by http://metabolomics.iiit.ac.in/ and we would like to thank them for their support.
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Maheshwary, S., Pudi, V. (2017). Mining Keystroke Timing Pattern for User Authentication. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., RaÅ›, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_14
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DOI: https://doi.org/10.1007/978-3-319-61461-8_14
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