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.
Original language | English |
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Article number | 9400816 |
Pages (from-to) | 57692-57703 |
Number of pages | 12 |
Journal | IEEE Access |
Volume | 9 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Autoencoder
- cryptography
- non-profiled
- preprocessing
- side-channel attacks
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Engineering(all)
- Electrical and Electronic Engineering