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Efficient Template Attacks Based on Probabilistic Multi-class Support Vector Machines

  • Timo Bartkewitz
  • Kerstin Lemke-Rust
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7771)

Abstract

Common template attacks are probabilistic relying on the multivariate Gaussian distribution regarding the noise of the device under attack. Though this is a realistic assumption, numerical problems are likely to occur in practice due to evaluation in higher dimensions. To avoid this, a feature selection is applied to identify points in time that contribute most information to an attack. An alternative to common template attacks is to apply machine learning in form of support vector machines (SVMs). Recent works brought out approaches that produce comparable results, respectively better in the presence of noise, but still not optimal in terms of efficiency and performance. In this work we show how to adapt the SVM template approach in order to considerably reduce the effort while carrying out the attack and how to better exploit the side-channel information under the assumption of an attack model with a strict order, e.g. Hamming weight model.

Keywords

Power Analysis Template Attacks Machine Learning Support Vector Machines 

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References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  3. 3.
    Brank, J., Grobelnik, M., Milic-Frayling, N., Mladenic, D.: Feature Selection Using Linear Support Vector Machines. No. MSR-TR-2002-63, Microsoft Research (2002)Google Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)Google Scholar
  7. 7.
    Duan, K.-B., Keerthi, S.S.: Which Is the Best Multiclass SVM Method? An Empirical Study. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 278–285. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Hospodar, G., De Mulder, E., Gierlichs, B., Vandewalle, J., Verbauwhede, I.: Least Squares Support Vector Machines for Side-Channel Analysis. In: COSADE 2011. CASED, Darmstadt (2011)Google Scholar
  11. 11.
    Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., Vandewalle, J.: Machine Learning in Side-channel Analysis: A First Study. Journal of Cryptographic Engineering 1, 293–302 (2011)CrossRefGoogle Scholar
  12. 12.
    Lerman, L., Bontempi, G., Markowitch, O.: Side Channel Attack: An Approach Based on Machine Learning. In: COSADE 2011. CASED, Darmstadt (2011)Google Scholar
  13. 13.
    Lin, H.T., Lin, C.J., Weng, R.C.: A Note on Platt’s Probabilistic Outputs for Support Vector Machines, vol. 68, pp. 267–276. Kluwer Academic Publishers, Hingham (2007)Google Scholar
  14. 14.
    Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  15. 15.
    Microchip Technology Inc.: PIC18F2420/2520/4420/4520 Data Sheet (2008)Google Scholar
  16. 16.
    Platt, J.C.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Advances in Kernel Methods, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  17. 17.
    Platt, J.C.: Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  21. 21.
    Zhang, Y., Schneider, J.G.: Projection Penalties: Dimension Reduction without Loss. In: Fürnkranz, J., Joachims, T. (eds.) ICML 2010, pp. 1223–1230. Omnipress (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Timo Bartkewitz
    • 1
  • Kerstin Lemke-Rust
    • 1
  1. 1.Department of Computer ScienceBonn-Rhine-Sieg University of Applied SciencesSankt AugustinGermany

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