Paper 2020/396
Improving Non-Profiled Side-Channel Attacks using Autoencoder based Preprocessing
Donggeun Kwon, HeeSeok Kim, and Seokhie Hong
Abstract
In recent years, deep learning-based side-channel attacks have established their position as mainstream. However, most deep learning techniques for cryptanalysis mainly focused on classifying side-channel information in a profiled scenario where attackers can obtain a label of training data. In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a non-profiling scenario. We also propose a new principle of training that trains an autoencoder through the noise from real data using noise-reduced labels. It notably diminishes the noise in measurements by modifying the autoencoder framework to the signal preprocessing. We present convincing comparisons on our custom dataset, captured from ChipWhisperer-Lite board, that demonstrate our approach outperforms conventional preprocessing methods such as principal component analysis and linear discriminant analysis. Furthermore, we apply the proposed methodology to realign de-synchronized traces that applied hiding countermeasures, and we experimentally validate the performance of the proposal. Finally, we experimentally show that we can improve the performance of higher-order side-channel attacks by using the proposed technique with domain knowledge for masking countermeasures.
Note: This paper is submitted a specific journal.
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Preprint. MINOR revision.
- Keywords
- Side-channel attackNon-profilingDeep learningAutoencoderPreprocessing
- Contact author(s)
- donggeun kwon @ gmail com
- History
- 2020-10-12: revised
- 2020-04-09: received
- See all versions
- Short URL
- https://ia.cr/2020/396
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/396,
author = {Donggeun Kwon and HeeSeok Kim and Seokhie Hong},
title = {Improving Non-Profiled Side-Channel Attacks using Autoencoder based Preprocessing},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/396},
year = {2020},
url = {https://eprint.iacr.org/2020/396}
}