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International Conference on Cryptology in Africa

AFRICACRYPT 2017: Progress in Cryptology - AFRICACRYPT 2017 pp 61–78Cite as

Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks

Part of the Lecture Notes in Computer Science book series (LNSC,volume 10239)

Abstract

Machine learning techniques represent a powerful paradigm in side-channel analysis, but they come with a price. Selecting the appropriate algorithm as well as the parameters can sometimes be a difficult task. Nevertheless, the results obtained usually justify such an effort. However, a large part of those results use simplification of the data relation and in fact do not consider allthe available information. In this paper, we analyze the hierarchical relation between the data and propose a novel hierarchical classification approach for side-channel analysis. With this technique, we are able to introduce two new attacks for machine learning side-channel analysis: Hierarchical attack and Structured attack. Our results show that both attacks can outperform machine learning techniques using the traditional approach as well as the template attack regarding accuracy. To support our claims, we give extensive experimental results and discuss the necessary conditions to conduct such attacks.

Keywords

  • Side-channel attacks
  • Profiled scenario
  • Machine learning techniques
  • Hierarchical classification
  • Hierarchical attack
  • Structured attack

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Fig. 1.
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Fig. 4.

Notes

  1. 1.

    Note that, an attacker could reveal the secret key with only one trace if it corresponds to HW 0 or 8, which occurs with a probability of \(\frac{2}{256}\).

  2. 2.

    For simplicity we assume that one key chunk is of the same size as one intermediate state chunk, however, this study can easily be extended for other scenarios as given e.g. in DES.

  3. 3.

    See e.g., in the hall of fame on [10].

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Acknowledgments

S. Picek was supported in part by Croatian Science Foundation under the project IP-2014-09-4882.

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Authors and Affiliations

  1. KU Leuven ESAT/COSIC and imec, Kasteelpark Arenberg 10, 3001, Leuven-Heverlee, Belgium

    Stjepan Picek

  2. IRISA/CNRS, Rennes, France

    Annelie Heuser

  3. University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia

    Alan Jovic

  4. IRISA/Inria, Rennes, France

    Axel Legay

Authors
  1. Stjepan Picek
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  2. Annelie Heuser
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  3. Alan Jovic
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  4. Axel Legay
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Corresponding author

Correspondence to Stjepan Picek .

Editor information

Editors and Affiliations

  1. NXP Semiconductors , San Jose, California, USA

    Marc Joye

  2. University of Caen , Caen, France

    Abderrahmane Nitaj

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Picek, S., Heuser, A., Jovic, A., Legay, A. (2017). Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks. In: Joye, M., Nitaj, A. (eds) Progress in Cryptology - AFRICACRYPT 2017. AFRICACRYPT 2017. Lecture Notes in Computer Science(), vol 10239. Springer, Cham. https://doi.org/10.1007/978-3-319-57339-7_4

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