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

Huanyu Wang; Elena Dubrova

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 attack CNN tandem model FPGA AES
Contact author(s): huanyu @ kth se
dubrova @ kth se
History: 2020-04-02: received
Short URL: https://ia.cr/2020/373
License: 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},
      url = {https://eprint.iacr.org/2020/373}
}