Exploring multi-task learning in the context of two masked AES implementations

Thomas Marquet; Elisabeth Oswald

Paper 2023/006

Exploring multi-task learning in the context of two masked AES implementations

Thomas Marquet, Universität Klagenfurt

Elisabeth Oswald

, Universität Klagenfurt

Abstract

This paper investigates different ways of applying multi-task learning in the context of two masked AES implementations (via the ASCAD-r and ASCAD-v2 databases). Enabled by multi-task learning, we propose novel architectures that significantly increase the consistency and performance of deep neural networks in a context where the attacker can not access the randomness of the countermeasures during profiling. Our work provides a wide range of experiments to understand the benefits of multi-task strategies against the current single-task state of the art. We show that multi-task learning is significantly more performant than single-task models on all our experiments. Furthermore, such strategies achieve novel milestones against protected implementations as we propose a new best attack on ASCAD-r and ASCAD-v2, along with models that defeat for the first time all masks of the affine masking on ASCAD-v2.

Metadata

Available format(s): PDF
Category: Attacks and cryptanalysis
Publication info: Preprint.
Keywords: Side Channel Attacks Masking Deep Learning Multi-task Learning
Contact author(s): thomas marquet @ aau at
elisabeth oswald @ aau at
History: 2023-09-10: last of 3 revisions; 2023-01-02: received; See all versions
Short URL: https://ia.cr/2023/006
License: CC BY

BibTeX

@misc{cryptoeprint:2023/006,
      author = {Thomas Marquet and Elisabeth Oswald},
      title = {Exploring multi-task learning in the context of two masked AES implementations},
      howpublished = {Cryptology ePrint Archive, Paper 2023/006},
      year = {2023},
      note = {\url{https://eprint.iacr.org/2023/006}},
      url = {https://eprint.iacr.org/2023/006}
}