Profiled Deep Learning Side-Channel Attack on a Protected Arbiter PUF Combined with Bitstream Modification

Yang Yu; Michail Moraitis; Elena Dubrova

Paper 2020/1031

Profiled Deep Learning Side-Channel Attack on a Protected Arbiter PUF Combined with Bitstream Modification

Yang Yu, Michail Moraitis, and Elena Dubrova

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. MINOR revision.
Keywords: Profiled attack deep learning side-channel analysis bitstream modification and arbiter PUF
Contact author(s): yang11 @ kth se
History: 2020-08-27: received
Short URL: https://ia.cr/2020/1031
License: CC BY

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},
      note = {\url{https://eprint.iacr.org/2020/1031}},
      url = {https://eprint.iacr.org/2020/1031}
}