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Keep It Unsupervised: Horizontal Attacks Meet Simple Classifiers

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Smart Card Research and Advanced Applications (CARDIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14530))

Abstract

In the last years, Deep Learning algorithms have been browsed and applied to Side-Channel Analysis in order to enhance attack’s performances. In some cases, the proposals came without an in-depth analysis allowing to understand the tool, its applicability scenarios, its limitations and the advantages it brings with respect to classical statistical tools. As an example, a study presented at CHES 2021 [16] proposed a corrective iterative framework to perform an unsupervised attack which achieves a \(100\%\) key bits recovery. In this paper we analyze the iterative framework and the datasets it was applied onto. The analysis suggests a much easier and interpretable way to both implement such an iterative framework and perform the attack using more conventional solutions, without affecting the attack’s performances.

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Notes

  1. 1.

    https://munacl.cryptojedi.org/curve25519-cortexm0.shtml.

  2. 2.

    More specifically, the swap condition value at the \(i^{th}\) iteration is equal to the XOR between the \(i^{th}\) bit and \((i-1)^{th}\) bit of the secret scalar.

  3. 3.

    Source code: https://github.com/AISyLab/IterativeDLFramework.

  4. 4.

    At each training phase, trainable paramaters are reset.

  5. 5.

    Regularization consists in applying a technique to avoid overfitting.

  6. 6.

    These results are those reported in [16] as we were not able to reproduce it using the source code.

References

  1. Aloise, D., Deshpande, A., Hansen, P., Popat, P.: Np-hardness of Euclidean sum-of-squares clustering. Mach. Learn. 75, 245–248 (2009). https://doi.org/10.1007/s10994-009-5103-0

    Article  Google Scholar 

  2. Arpit, D., et al.: A closer look at memorization in deep networks (2017)

    Google Scholar 

  3. Cagli, E., Dumas, C., Prouff, E.: Enhancing dimensionality reduction methods for side-channel attacks. In: Homma, N., Medwed, M. (eds.) CARDIS 2015. LNCS, vol. 9514, pp. 15–33. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31271-2_2

    Chapter  Google Scholar 

  4. Choudary, O., Kuhn, M.G.: Efficient template attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 253–270. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08302-5_17

    Chapter  Google Scholar 

  5. Düll, M., et al.: High-speed curve25519 on 8-bit, 16-bit, and 32-bit microcontrollers. Des. Codes Cryptography 77 (2015). https://doi.org/10.1007/s10623-015-0087-1

  6. Du, Q., Faber, V., Gunzburger, M.: Centroidal Voronoi tessellations: applications and algorithms. SIAM Rev. 41(4), 637–676 (1999)

    Article  MathSciNet  Google Scholar 

  7. Gallier, J.: Dirichlet–Voronoi diagrams and Delaunay triangulations. In: Gallier, J. (ed.) Geometric Methods and Applications. Texts in Applied Mathematics, vol. 38, pp. 301–319. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-9961-0_10

    Chapter  Google Scholar 

  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, Cham (2006). https://doi.org/10.1007/11894063_2

    Chapter  Google Scholar 

  9. Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels (2018)

    Google Scholar 

  10. Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980). https://doi.org/10.1109/TCOM.1980.1094577

    Article  Google Scholar 

  11. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982). https://doi.org/10.1109/TIT.1982.1056489

    Article  MathSciNet  Google Scholar 

  12. van der Maaten, L., Hinton, G.: Viualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    Google Scholar 

  13. Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) SPACE 2016. LNCS, vol. 10076, pp. 3–26. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49445-6_1

    Chapter  Google Scholar 

  14. Nascimento, E., Chmielewski, L.: Applying horizontal clustering side-channel attacks on embedded ECC implementations. In: Eisenbarth, T., Teglia, Y. (eds.) CARDIS 2017. LNCS, vol. 10728, pp. 213–231. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-75208-2_13

    Chapter  Google Scholar 

  15. Perin, G., Chmielewski, Ł: A semi-parametric approach for side-channel attacks on protected RSA implementations. In: Homma, N., Medwed, M. (eds.) CARDIS 2015. LNCS, vol. 9514, pp. 34–53. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31271-2_3

    Chapter  Google Scholar 

  16. Perin, G., Chmielewski, U., Batina, L., Picek, S.: Keep it unsupervised: horizontal attacks meet deep learning. IACR Trans. Cryptographic Hardware Embed. Syst. 2021(1), 343–372 (2020). https://doi.org/10.46586/tches.v2021.i1.343-372. https://tches.iacr.org/index.php/TCHES/article/view/8737

  17. Song, H., Kim, M., Park, D., Shin, Y., Lee, J.G.: Learning from noisy labels with deep neural networks: a survey (2022)

    Google Scholar 

  18. Ying, X.: An overview of overfitting and its solutions. In: Journal of Physics: Conference Series, vol. 1168, no. 2, p. 022022 (2019). https://doi.org/10.1088/1742-6596/1168/2/022022

  19. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization (2017)

    Google Scholar 

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Acknowledgments

The authors would like to thank Guilherme Perin and the anonymous reviewers for their valuable comments which helped to improve this work. We would also like to thank Simon Abelard, Eleonora Cagli, Julien Eynard, Antoine Loiseau, Ange Martinelli, Guénaël Renault and Gabriel Zaid for fruitful discussions about this work. This work was financially supported by the defense innovation agency (AID) from the French ministry of armed forces.

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Correspondence to Sana Boussam or Ninon Calleja Albillos .

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Boussam, S., Albillos, N.C. (2024). Keep It Unsupervised: Horizontal Attacks Meet Simple Classifiers. In: Bhasin, S., Roche, T. (eds) Smart Card Research and Advanced Applications. CARDIS 2023. Lecture Notes in Computer Science, vol 14530. Springer, Cham. https://doi.org/10.1007/978-3-031-54409-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-54409-5_11

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