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Deep Stacking Ensemble Learning Applied to Profiling Side-Channel Attacks

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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))

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Abstract

Deep Learning is nowadays widely used by security evaluators to conduct side-channel attacks, especially in profiling attacks that allow a supervised learning phase. However, designing an efficient neural network model in a side-channel attack context can be a difficult task that may require a laborious hyperparameterization process. Hyperparameter selection is known to be a challenging problem in Deep Learning, while being a crucial factor for neural networks performances. Recent works investigate the so-called Deep Ensemble Learning in the side-channel context. It consists in using multiple neural networks in a single predictive task and aggregating the several predictions in an opportune way. The intuition behind is to use the power of numbers to improve the attack performance. In this work, we propose to use Stacking as an aggregation method, in which a meta-model is trained to learn the best way to combine the output class probabilities of the ensemble networks. Our proposal is supported by several experimental results, that allow to conclude that the use of Stacking can relieve the security evaluator from performing a fine hyperparameterization.

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Notes

  1. 1.

    There are other ensemble methods, in particular the Boosting [9], which we have experimented without obtaining good enough performance for the considered datasets.

  2. 2.

    Other criteria has been tested during the experimental campaign, but the obtained results were less performant and uninteresting in our opinion. Thus, they have been omitted.

  3. 3.

    We also tried to train on the validation dataset, but the results were generally worse due to the lack of data. Results have thus been omitted.

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Acknowledgements

This work was financially supported by the Defense Innovation Agency (AID) from the french ministry of armed forces.

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Authors and Affiliations

  1. Univ. Grenoble Alpes, 38000, Grenoble, France

    Dorian Llavata & Eleonora Cagli

  2. CEA, LETI, MINATEC Campus, 38054, Grenoble, France

    Dorian Llavata & Eleonora Cagli

  3. Univ. Jean Monnet Saint-Etienne, CNRS, Institut d Optique Graduate School, Lab. Hubert Curien UMR 5516, 42023, Saint-Etienne, France

    Dorian Llavata, Rémi Eyraud, Vincent Grosso & Lilian Bossuet

Authors
  1. Dorian Llavata
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  2. Eleonora Cagli
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  3. Rémi Eyraud
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  4. Vincent Grosso
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  5. Lilian Bossuet
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Correspondence to Dorian Llavata .

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Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Shivam Bhasin

  2. NinjaLab, Montpellier, France

    Thomas Roche

A Weak models

A Weak models

(See Fig. 7).

Fig. 7.
figure 7

Search space for weak models.

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Llavata, D., Cagli, E., Eyraud, R., Grosso, V., Bossuet, L. (2024). Deep Stacking Ensemble Learning Applied to Profiling Side-Channel Attacks. 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_12

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  • DOI: https://doi.org/10.1007/978-3-031-54409-5_12

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  • Print ISBN: 978-3-031-54408-8

  • Online ISBN: 978-3-031-54409-5

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