Paper 2023/1100
Shift-invariance Robustness of Convolutional Neural Networks in Side-channel Analysis
, Delft University of Technology, Radboud University NijmegenAbstract
Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure is commonly investigated in related works - desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We propose to use data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show the proposed techniques work very well and improve the attack significantly, even for an order of magnitude.
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Preprint.
- Keywords
- Side-channel AnalysisDeep learningMisalignmentCountermeasuresShift-invariance
- Contact author(s)
-
m krcek @ tudelft nl
lichao wu @ ru nl
g perin @ liacs leidenuniv nl
stjepan picek @ ru nl - History
- 2023-07-17: approved
- 2023-07-14: received
- See all versions
- Short URL
- https://ia.cr/2023/1100
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1100,
author = {Marina Krček and Lichao Wu and Guilherme Perin and Stjepan Picek},
title = {Shift-invariance Robustness of Convolutional Neural Networks in Side-channel Analysis},
howpublished = {Cryptology ePrint Archive, Paper 2023/1100},
year = {2023},
note = {\url{https://eprint.iacr.org/2023/1100}},
url = {https://eprint.iacr.org/2023/1100}
}
