Abstract
We consider the problem of identifying a user typing on a computer keyboard based on patterns in the time series consisting of keyboard events. We develop a learning algorithm, which can rather accurately learn to authenticate and protect users. Our solution is based on a simple extension of the well known Lempel-Ziv (78) universal compression algorithm. A novel application of our results is a second-layer behaviometric security system, which continually examines the current user without interfering with this user’s work while attempting to identify unauthorized users pretending to be the user. We study the utility of our methods over a real dataset consisting of 5 users and 30 ‘attackers’.
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Matyas Jr., V., Riha, Z.: Biometric authentication systems. Technical report, ECOM-MONITOR (2000)
Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems 16(4), 351–359 (2000)
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992)
Willems, F.M.J., Shtarkov, Y.M., Tjalkens, T.J.: The context-tree weighting method: basic properties. In: IEEE Trans. Info. Theory, pp. 653–664 (1995)
Manzini, G.: The burrows-wheeler transform: Theory and practice. In: Kutyłowski, M., Wierzbicki, T., Pacholski, L. (eds.) MFCS 1999. LNCS, vol. 1672, pp. 34–47. Springer, Heidelberg (1999)
Cleary, J.G., Teahan, W.J.: Unbounded length contexts for PPM. The Computer Journal 40(2/3), 67–75 (1997)
Ziv, J., Lempel, A.: Compression of individual sequences via variable rate coding. IEEE Transactions on Information Theory 24, 530–536 (1978)
Langdon, G.G.: A note on the lempel-ziv model for compressing individual sequences. IEEE Transactions on Information Theory 29, 284–287 (1983)
Feder, M.: Gambling using a finite state machine. IEEE Transactions on Information Theory 37, 1459–1465 (1991)
Stirzaker, D.R., Grimmett, G.R.: Probability and Random Processes, 3rd edn. Oxford University Press, Oxford (2002)
Gaines, R., Lisowski, W., Press, S., Shapiro, W.: Authentication by keystroke timing: Some preliminary results. Report R-256-NSF, Rand Corp. (1980)
Bergadano, F., Gunetti, D., Picardi, C.: User authentification through keystroke dynamics. ACM Transactions on Information and System Security 5(4), 367–397 (2002)
Bishop, C.: Novelty detection and neural network validation. IEEE Proceedings on Vision, Image and Signal Processing 141(4), 217–222 (1994)
Japkowicz, N.: Concept-Learning in the absence of counterexamples: an autoassociation-based approach to classification. PhD thesis, Rutgers, New Brunswick (1999)
Tax, D.M.J.: One-Class Classification. PhD thesis, The Delft University of Technology (2001)
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Nisenson, M., Yariv, I., El-Yaniv, R., Meir, R. (2003). Towards Behaviometric Security Systems: Learning to Identify a Typist. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_33
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DOI: https://doi.org/10.1007/978-3-540-39804-2_33
Publisher Name: Springer, Berlin, Heidelberg
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