Paper 2020/373 Original: eprint.iacr.org/2020/373 ↗
Tandem Deep Learning Side-Channel Attack Against FPGA Implementation of AES
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.
- History
- 2020-04-02: last updated
- 2020-04-02: published
- Short URL
- https://ia.cr/2020/373
- License
- https://creativecommons.org/licenses/by/4.0/
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}
}