Abstract
The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack is reputed as the strongest power analysis attack, they effectiveness is strongly dependent on the validity of the Gaussian assumption. This led recently to the appearance of nonparametric approaches, often based on machine learning strategies. Though these approaches outperform template attack, they tend to neglect the potential source of information available in the temporal dependencies between power values. In this paper, we propose an original multi-class profiling attack that takes into account the temporal dependence of power traces. The experimental study shows that the time series analysis approach is competitive and often better than static classification alternatives.
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Aha, D.W.: Editorial. Artificial Intelligence Review 11, 7–10 (1997)
Archambeau, C., Peeters, E., Standaert, F.-X., Quisquater, J.-J.: Template attacks in principal subspaces. In: Goubin, L., Matsui, M. (eds.) CHES 2006. LNCS, vol. 4249, pp. 1–14. Springer, Heidelberg (2006)
Bartkewitz, T., Lemke-Rust, K.: Efficient template attacks based on probabilistic multi-class support vector machines. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 263–276. Springer, Heidelberg (2013)
Bellman, R.: Dynamic Programming, 1st edn. Princeton University Press, Princeton (1957)
Birattari, M., Bontempi, G.: Lazy: Lazy Learning for Local Regression, R package version 1.2-14 (2003)
Birattari, M., Bontempi, G., Bersini, H.: Lazy learning meets the recursive least squares algorithm. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II, pp. 375–381. MIT Press, Cambridge (1999)
Bisgaard, S., Kulahci, M.: Time Series Analysis and Forecasting by Example. Wiley Series in Probability and Statistics. John Wiley Sons (2011)
Bontempi, G., Birattari, M., Bersini, H.: Lazy learners at work: The lazy learning toolbox. In: EUFIT 1999: The 7th European Congress on Intelligent Techniques and Soft Computing, Abstract Booklet with CD Rom, Aachen, Germany. ELITE Foundation (1999)
Bontempi, G., Birattari, M., Bersini, H.: Lazy Learning: A local method for supervised learning. In: Jain, L.C., Kacprzyk, J. (eds.) New Learning Paradigms in Soft Computing, pp. 97–137. Springer, Heidelberg (2001)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Chari, S., Rao, J., Rohatgi, P.: Template attacks. In: Kaliski Jr., B.S., Koç, Ç.K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 13–28. Springer, Heidelberg (2003)
Coron, J.-S., Naccache, D., Kocher, P.: Statistics and secret leakage. ACM Trans. Embed. Comput. Syst. 3, 492–508 (2004)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 273–297 (1995)
Elaabid, M.A., Meynard, O., Guilley, S., Danger, J.-L.: Combined side-channel attacks. In: Chung, Y., Yung, M. (eds.) WISA 2010. LNCS, vol. 6513, pp. 175–190. Springer, Heidelberg (2011)
Gandolfi, K., Mourtel, C., Olivier, F.: Electromagnetic Analysis: Concrete Results. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) CHES 2001. LNCS, vol. 2162, pp. 251–261. Springer, Heidelberg (2001)
Gierlichs, B., Batina, L., Tuyls, P., Preneel, B.: Mutual Information Analysis - A Generic Side-Channel Distinguisher. In: Oswald, E., Rohatgi, P. (eds.) CHES 2008. LNCS, vol. 5154, pp. 426–442. Springer, Heidelberg (2008)
Gierlichs, B., Lemke-Rust, K., Paar, C.: Templates vs. stochastic methods. In: Goubin, L., Matsui, M. (eds.) CHES 2006. LNCS, vol. 4249, pp. 15–29. Springer, Heidelberg (2006)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer (2009)
Heuser, A., Zohner, M.: Intelligent machine homicide - Breaking cryptographic devices using support vector machines. In: Schindler, W., Huss, S.A. (eds.) COSADE 2012. LNCS, vol. 7275, pp. 249–264. Springer, Heidelberg (2012)
Hospodar, G., Gierlichs, B., Mulder, E.D., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptographic Engineering 1(4), 293–302 (2011)
Hospodar, G., Mulder, E.D., Gierlichs, B., Vandewalle, J., Verbauwhede, I.: Least Squares Support Vector Machines for Side-Channel Analysis, pp. 99–104. Center for Advanced Security Research Darmstadt (2011)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. Trans. Neur. Netw. 13(2), 415–425 (2002)
Kocher, P.C.: Timing attacks on implementations of diffie-hellman, RSA, DSS, and other systems. In: Koblitz, N. (ed.) CRYPTO 1996. LNCS, vol. 1109, pp. 104–113. Springer, Heidelberg (1996)
Kocher, P.C., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999)
KreBel, U.H.-G.: Pairwise classification and support vector machines. In: Advances in Kernel Methods, pp. 255–268. MIT Press, Cambridge (1999)
Lerman, L., Bontempi, G., Markowitch, O.: Side Channel Attack: an Approach Based on Machine Learning, pp. 29–41. Center for Advanced Security Research Darmstadt (2011)
Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. International Journal of Applied Cryptography (to appear, 2013)
Lerman, L., Bontempi, G., Markowitch, O.: sideChannelAttack: Side Channel Attack, R package version 1.0-7 (2013)
Lerman, L., Fernandes Medeiros, S., Veshchikov, N., Meuter, C., Bontempi, G., Markowitch, O.: Semi-supervised template attack. In: Prouff, E. (ed.) COSADE 2013. LNCS, vol. 7864, pp. 184–199. Springer, Heidelberg (2013)
Makridakis, S., Wheelwright, S., Hyndman, R.J.: Forecasting: Methods and Applications. Wiley series in management. Wiley (1998)
Mangard, S., Oswald, E., Popp, T.: Power analysis attacks - revealing the secrets of smart cards. Springer (2007)
Oren, Y., Renauld, M., Standaert, F.-X., Wool, A.: Algebraic side-channel attacks beyond the hamming weight leakage model. In: Prouff, E., Schaumont, P. (eds.) CHES 2012. LNCS, vol. 7428, pp. 140–154. Springer, Heidelberg (2012)
Oswald, E., Mangard, S.: Template Attacks on Masking-Resistance Is Futile. In: Abe, M. (ed.) CT-RSA 2007. LNCS, vol. 4377, pp. 243–256. Springer, Heidelberg (2006)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Rechberger, C., Oswald, E.: Practical template attacks. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 440–456. Springer, Heidelberg (2005)
Reparaz, O., Gierlichs, B., Verbauwhede, I.: Selecting time samples for multivariate DPA attacks. In: Prouff, E., Schaumont, P. (eds.) CHES 2012. LNCS, vol. 7428, pp. 155–174. Springer, Heidelberg (2012)
Rivain, M., Dottax, E., Prouff, E.: Block ciphers implementations provably secure against second order side channel analysis. In: Nyberg, K. (ed.) FSE 2008. LNCS, vol. 5086, pp. 127–143. Springer, Heidelberg (2008)
DPAContest V1 (February 2013), http://www.dpacontest.org/home/
Wallace, B.C., Dahabreh, I.J.: Class probability estimates are unreliable for imbalanced data (and how to fix them). In: Zaki, M.J., Siebes, A., Yu, J.X., Goethals, B., Webb, G.I., Wu, X. (eds.) ICDM, pp. 695–704. IEEE Computer Society (2012)
Whitnall, C., Oswald, E., Mather, L.: An exploration of the kolmogorov-smirnov test as a competitor to mutual information analysis. In: Prouff, E. (ed.) CARDIS 2011. LNCS, vol. 7079, pp. 234–251. Springer, Heidelberg (2011)
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Lerman, L., Bontempi, G., Ben Taieb, S., Markowitch, O. (2013). A Time Series Approach for Profiling Attack. In: Gierlichs, B., Guilley, S., Mukhopadhyay, D. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2013. Lecture Notes in Computer Science, vol 8204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41224-0_7
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