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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))


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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    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.

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    Source code:

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    At each training phase, trainable paramaters are reset.

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    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.


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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.

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  • Print ISBN: 978-3-031-54408-8

  • Online ISBN: 978-3-031-54409-5

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