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Erschienen in:
Improving Non-profiled Attacks on Exponentiations Based on Clustering and Extracting Leakage from Multi-channel High-Resolution EM Measurements Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis)

verfasst von : Liran Lerman, Romain Poussier, Gianluca Bontempi, Olivier Markowitch, François-Xavier Standaert

Erschienen in: Constructive Side-Channel Analysis and Secure Design

Verlag: Springer International Publishing

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Abstract

Template attacks and machine learning are two popular approaches to profiled side-channel analysis. In this paper, we aim to contribute to the understanding of their respective strengths and weaknesses, with a particular focus on their curse of dimensionality. For this purpose, we take advantage of a well-controlled simulated experimental setting in order to put forward two important intuitions. First and from a theoretical point of view, the data complexity of template attacks is not sensitive to the dimension increase in side-channel traces given that their profiling is perfect. Second and from a practical point of view, concrete attacks are always affected by (estimation and assumption) errors during profiling. As these errors increase, machine learning gains interest compared to template attacks, especially when based on random forests.

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Vorheriges Kapitel Improving Non-profiled Attacks on Exponentiations Based on Clustering and Extracting Leakage from Multi-channel High-Resolution EM Measurements
Nächstes Kapitel Efficient Selection of Time Samples for Higher-Order DPA with Projection Pursuits
Fußnoten
1
Note that the gain of LR-based attacks over TA is known and has been analyzed, e.g. in [10, 23]. Namely, it essentially depends on the size of the basis used in LR.
 
2
There are indeed variants of SVM and RF that aim to remedy to this issue. Yet, the “probability-like” scores they output are not directly exploitable in the estimation of information theoretic metrics either. For example, we could exhibit examples where probability-like scores of one do not correspond to a success rate of one.
 
Literatur
1.
Banciu, V., Oswald, E., Whitnall, C.: Reliable information extraction for single trace attacks. IACR Cryptology ePrint Archive, 2015:45 (2015)
2.
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) CrossRef
3.
4.
Chari, S., Rao, J.R., 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 (2002)CrossRef
5.
Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 253–270. Springer, Heidelberg (2014)
6.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
7.
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2010)
8.
Durvaux, F., Standaert, F.-X., Veyrat-Charvillon, N.: How to certify the leakage of a chip? In: Nguyen, P.Q., Oswald, E. (eds.) EUROCRYPT 2014. LNCS, vol. 8441, pp. 459–476. Springer, Heidelberg (2014) CrossRef
9.
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) CrossRef
10.
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) CrossRef
11.
Heuser, A., Zohner, M.: Intelligent machine homicide. In: Schindler, W., Huss, S.A. (eds.) COSADE 2012. LNCS, vol. 7275, pp. 249–264. Springer, Heidelberg (2012) CrossRef
12.
Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine learning in side-channel analysis: a first study. J. Cryptographic Eng. 1(4), 293–302 (2011)CrossRef
13.
Hospodar, G., De Mulder, E., Gierlichs, B., Vandewalle, J., Verbauwhede, I.: Least squares support vector machines for side-channel analysis. In: Second International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 99–104. Center for Advanced Security Research Darmstadt (2011)
14.
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)
15.
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) CrossRef
16.
Lerman, L., Bontempi, G., Markowitch, O.: Side-channel attacks: an approach based on machine learning. In: Second International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 29–41. Center for Advanced Security Research Darmstadt (2011)
17.
Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. IJACT 3(2), 97–115 (2014)MathSciNetCrossRef
18.
Mangard, S., Oswald, E., Standaert, F.-X.: One for all - all for one: unifying standard differential power analysis attacks. IET Inf. Secur. 5(2), 100–110 (2011)CrossRef
19.
Patel, H., Baldwin, R.O.: Random forest profiling attack on advanced encryption standard. IJACT 3(2), 181–194 (2014)MathSciNetCrossRef
20.
Renauld, M., Standaert, F.-X., Veyrat-Charvillon, N., Kamel, D., Flandre, D.: A formal study of power variability issues and side-channel attacks for nanoscale devices. In: Paterson, K.G. (ed.) EUROCRYPT 2011. LNCS, vol. 6632, pp. 109–128. Springer, Heidelberg (2011) CrossRef
21.
Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. Series in machine perception and artificial intelligence. World Scientific Publishing Company, Incorporated, Singapore (2008)
22.
Schindler, W., Lemke, K., Paar, C.: A stochastic model for differential side channel cryptanalysis. In: Rao, J.R., Sunar, B. (eds.) CHES 2005. LNCS, vol. 3659, pp. 30–46. Springer, Heidelberg (2005) CrossRef
23.
Standaert, F.-X., Koeune, F., Schindler, W.: How to compare profiled side-channel attacks? In: Abdalla, M., Pointcheval, D., Fouque, P.-A., Vergnaud, D. (eds.) ACNS 2009. LNCS, vol. 5536, pp. 485–498. Springer, Heidelberg (2009) CrossRef
24.
Standaert, F.-X., Malkin, T.G., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 443–461. Springer, Heidelberg (2009) CrossRef
25.
Veyrat-Charvillon, N., Gérard, B., Renauld, M., Standaert, F.-X.: An optimal key enumeration algorithm and its application to side-channel attacks. In: Knudsen, L.R., Wu, H. (eds.) SAC 2012. LNCS, vol. 7707, pp. 390–406. Springer, Heidelberg (2013) CrossRef
Metadaten
Titel
Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis)
verfasst von
Liran Lerman
Romain Poussier
Gianluca Bontempi
Olivier Markowitch
François-Xavier Standaert
Copyright-Jahr
2015
Buch
Constructive Side-Channel Analysis and Secure Design

Print ISBN: 978-3-319-21475-7

Electronic ISBN: 978-3-319-21476-4

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-21476-4_2