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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)


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.


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

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    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 \(\frac{2}{256}\).

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

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Correspondence to Stjepan Picek .

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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.

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