Paper 2020/1031 Original: eprint.iacr.org/2020/1031 ↗
Profiled Deep Learning Side-Channel Attack on a Protected Arbiter PUF Combined with Bitstream Modification
Abstract
In this paper we show that deep learning can be used to identify the shape of power traces corresponding to the responses of a protected arbiter PUF implemented in FPGAs. To achieve that, we combine power analysis with bitstream modification. We train a CNN classifier on two 28nm XC7 FPGAs implementing 128-stage arbiter PUFs and then classify the responses of PUFs from two other FPGAs. We demonstrate that it is possible to reduce the number of traces required for a successful attack to a single trace by modifying the bitstream to replicate PUF responses.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2020-08-27: last updated
- 2020-08-27: published
- Short URL
- https://ia.cr/2020/1031
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2020/1031,
author = {Yang Yu and Michail Moraitis and Elena Dubrova},
title = {Profiled Deep Learning Side-Channel Attack on a Protected Arbiter PUF Combined with Bitstream Modification},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/1031},
year = {2020},
url = {https://eprint.iacr.org/2020/1031}
}