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

  • Liran Lerman
  • Romain Poussier
  • Gianluca Bontempi
  • Olivier Markowitch
  • François-Xavier Standaert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9064)

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.

Keywords

Support Vector Machine Random Forest Template Attack Attack Trace Leakage Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

F.-X. Standaert is a research associate of the Belgian Fund for Scientific Research (FNRS-F.R.S.). This work has been funded in parts by the European Commission through the ERC project 280141 (CRASH).

References

  1. 1.
    Banciu, V., Oswald, E., Whitnall, C.: Reliable information extraction for single trace attacks. IACR Cryptology ePrint Archive, 2015:45 (2015)Google Scholar
  2. 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) CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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) Google Scholar
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)Google Scholar
  7. 7.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2010)Google Scholar
  8. 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) CrossRefGoogle Scholar
  9. 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) CrossRefGoogle Scholar
  10. 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) CrossRefGoogle Scholar
  11. 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) CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)Google Scholar
  14. 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) Google Scholar
  15. 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) CrossRefGoogle Scholar
  16. 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)Google Scholar
  17. 17.
    Lerman, L., Bontempi, G., Markowitch, O.: Power analysis attack: an approach based on machine learning. IJACT 3(2), 97–115 (2014)MathSciNetCrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Patel, H., Baldwin, R.O.: Random forest profiling attack on advanced encryption standard. IJACT 3(2), 181–194 (2014)MathSciNetCrossRefGoogle Scholar
  20. 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) CrossRefGoogle Scholar
  21. 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) Google Scholar
  22. 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) CrossRefGoogle Scholar
  23. 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) CrossRefGoogle Scholar
  24. 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) CrossRefGoogle Scholar
  25. 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) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Liran Lerman
    • 1
  • Romain Poussier
    • 2
  • Gianluca Bontempi
    • 1
  • Olivier Markowitch
    • 1
  • François-Xavier Standaert
    • 2
  1. 1.Département d’informatiqueUniversité Libre de BruxellesBrusselsBelgium
  2. 2.ICTEAM/INGIUniversité catholique de LouvainLouvain-la-NeuveBelgium

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