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Cryptography and Communications
Erschienen in: Cryptography and Communications 4/2021

29.04.2021

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

verfasst von: Charles-Henry Bertrand Van Ouytsel, Olivier Bronchain, Gaëtan Cassiers, François-Xavier Standaert

Erschienen in: Cryptography and Communications | Ausgabe 4/2021

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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).
Vorheriger Artikel Monomial evaluation of polynomial functions protected by threshold implementations—with an illustration on AES—
Nächster Artikel A stealthy Hardware Trojan based on a Statistical Fault Attack

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Fußnoten
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.
 
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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Metadaten
Titel
How to fool a black box machine learning based side-channel security evaluation
verfasst von
Charles-Henry Bertrand Van Ouytsel
Olivier Bronchain
Gaëtan Cassiers
François-Xavier Standaert
Publikationsdatum
29.04.2021
Verlag
Springer US
Erschienen in
Cryptography and Communications / Ausgabe 4/2021
Print ISSN: 1936-2447
Elektronische ISSN: 1936-2455
DOI
https://doi.org/10.1007/s12095-021-00479-x

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