Paper 2020/373

Tandem Deep Learning Side-Channel Attack Against FPGA Implementation of AES

Huanyu Wang and Elena Dubrova

Abstract

The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to recover the key. The potential benefits of combining multiple classifiers have not been explored yet in the side-channel attack's context. In this paper, we show that, by combining several CNN classifiers which use different attack points, it is possible to considerably reduce (more than 40% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.

Metadata
Available format(s)
PDF
Category
Secret-key cryptography
Publication info
Preprint. MINOR revision.
Keywords
side-channel attackCNNtandem modelFPGAAES
Contact author(s)
huanyu @ kth se
dubrova @ kth se
History
2020-04-02: received
Short URL
https://ia.cr/2020/373
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2020/373,
      author = {Huanyu Wang and Elena Dubrova},
      title = {Tandem Deep Learning Side-Channel Attack Against FPGA Implementation of AES},
      howpublished = {Cryptology ePrint Archive, Paper 2020/373},
      year = {2020},
      note = {\url{https://eprint.iacr.org/2020/373}},
      url = {https://eprint.iacr.org/2020/373}
}
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