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Journal of Cryptographic Engineering

16.12.2023 | Research Article

Keep it unbiased: a comparison between estimation of distribution algorithms and deep learning for human interaction-free side-channel analysis

verfasst von: Unai Rioja, Lejla Batina, Igor Armendariz, Jose Luis Flores

Erschienen in: Journal of Cryptographic Engineering

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Abstract

Evaluating side-channel analysis (SCA) security is a complex process, involving applying several techniques whose success depends on human engineering. Therefore, it is crucial to avoid a false sense of confidence provided by non-optimal (failing) attacks. Different alternatives have emerged lately trying to mitigate human dependency, among which deep learning (DL) attacks are the most studied today. DL promise to simplify the procedure by e.g. evading the need for point of interest (POI) selection, among other shortcuts. However, including DL in the equation comes at a price, since working with neural networks is not straightforward in this context. Recently, an alternative has appeared with the potential to mitigate this dependence without adding extra complexity: estimation of distribution algorithm-based SCA. From the perspective of avoiding the need for POI selection, in this paper we provide a comparison of the two relevant methods. Our findings are supported by experimental results on various datasets.

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Fußnoten
3
Note that a second-order attack (combining the leakage of two bytes of the key at a time to remove the mask) is feasible in both situations [39].
 
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Metadaten
Titel
Keep it unbiased: a comparison between estimation of distribution algorithms and deep learning for human interaction-free side-channel analysis
verfasst von
Unai Rioja
Lejla Batina
Igor Armendariz
Jose Luis Flores
Publikationsdatum
16.12.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Cryptographic Engineering
Print ISSN: 2190-8508
Elektronische ISSN: 2190-8516
DOI
https://doi.org/10.1007/s13389-023-00342-0

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