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How to fool a black box machine learning based side-channel security evaluation

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Abstract

Machine learning and deep learning algorithms are increasingly considered as potential candidates to perform black box side-channel security evaluations. Inspired by the literature on machine learning security, we put forward that it is easy to conceive implementations for which such black box security evaluations will incorrectly conclude that recovering the key is difficult, while an informed evaluator / adversary will reach the opposite conclusion (i.e., that the device is insecure given the amount of measurements available).

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Notes

  1. Less relevant examples for the following discussion include model stealing [20] and membership inference attacks [21]

  2. Which applies to non-profiled machine learning based evaluations as well [26].

  3. Other intermediate computations could be targeted (e.g., the output of AddRoundKey). Yet, the output of the Sbox offers a sweat spot for side-channel attacks due to its non-linearity.

  4. http://satoh.cs.uec.ac.jp/SAKURA/hardware/SAKURA-X.html

  5. https://github.com/hgrosz/aes-dom

  6. This approach can directly be applied to bitslice masked ciphers [38]. Indeed, the protected implementation can be placed on the lower bits and the cheating labels on the upper bits with disabled randomness. This will make the upper bits leaking at first order exactly as in the hardware case.

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Acknowledgements

Charles-Henry Bertrand Van Ouytsel, Gaëtan Cassiers and François-Xavier Standaert are respectively FRIA grantee, Research Fellow and Senior Associate Researcher of the Belgian Fund for Scientific Research (FNRS-F.R.S.). This work has been funded in part by the ERC project 724725.

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  1. ICTEAM Institute, UCLouvain, Louvain-la-Neuve, Belgium

    Charles-Henry Bertrand Van Ouytsel, Olivier Bronchain, Gaëtan Cassiers & François-Xavier Standaert

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  1. Charles-Henry Bertrand Van Ouytsel
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Correspondence to Olivier Bronchain.

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Bertrand Van Ouytsel, CH., Bronchain, O., Cassiers, G. et al. How to fool a black box machine learning based side-channel security evaluation. Cryptogr. Commun. 13, 573–585 (2021). https://doi.org/10.1007/s12095-021-00479-x

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