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International Workshop on Constructive Side-Channel Analysis and Secure Design

COSADE 2015: Constructive Side-Channel Analysis and Secure Design pp 20–33Cite as

Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis)

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9064))

Abstract

Template attacks and machine learning are two popular approaches to profiled side-channel analysis. In this paper, we aim to contribute to the understanding of their respective strengths and weaknesses, with a particular focus on their curse of dimensionality. For this purpose, we take advantage of a well-controlled simulated experimental setting in order to put forward two important intuitions. First and from a theoretical point of view, the data complexity of template attacks is not sensitive to the dimension increase in side-channel traces given that their profiling is perfect. Second and from a practical point of view, concrete attacks are always affected by (estimation and assumption) errors during profiling. As these errors increase, machine learning gains interest compared to template attacks, especially when based on random forests.

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Notes

  1. 1.

    Note that the gain of LR-based attacks over TA is known and has been analyzed, e.g. in [10, 23]. Namely, it essentially depends on the size of the basis used in LR.

  2. 2.

    There are indeed variants of SVM and RF that aim to remedy to this issue. Yet, the “probability-like” scores they output are not directly exploitable in the estimation of information theoretic metrics either. For example, we could exhibit examples where probability-like scores of one do not correspond to a success rate of one.

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Acknowledgements

F.-X. Standaert is a research associate of the Belgian Fund for Scientific Research (FNRS-F.R.S.). This work has been funded in parts by the European Commission through the ERC project 280141 (CRASH).

Author information

Authors and Affiliations

  1. Département d’informatique, Université Libre de Bruxelles, Brussels, Belgium

    Liran Lerman, Gianluca Bontempi & Olivier Markowitch

  2. ICTEAM/INGI, Université catholique de Louvain, Louvain-la-Neuve, Belgium

    Romain Poussier & François-Xavier Standaert

Authors
  1. Liran Lerman
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  2. Romain Poussier
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  3. Gianluca Bontempi
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  4. Olivier Markowitch
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  5. François-Xavier Standaert
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Corresponding author

Correspondence to Liran Lerman .

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Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Stefan Mangard

  2. NXP Semiconductors Germany GmbH, Hamburg, Germany

    Axel Y. Poschmann

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Lerman, L., Poussier, R., Bontempi, G., Markowitch, O., Standaert, FX. (2015). Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis). In: Mangard, S., Poschmann, A. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2015. Lecture Notes in Computer Science(), vol 9064. Springer, Cham. https://doi.org/10.1007/978-3-319-21476-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-21476-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21475-7

  • Online ISBN: 978-3-319-21476-4

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