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Non-Profiled Deep Learning-Based Side-Channel Preprocessing With Autoencoders

Authors
Kwon, DonggeunKim, HeeseokHong, Seokhie
Issue Date
2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Side-channel attacks; Deep learning; Performance evaluation; Noise reduction; Correlation; Training; Noise measurement; Autoencoder; side-channel attacks; non-profiled; preprocessing; cryptography
Citation
IEEE ACCESS, v.9, pp 57692 - 57703
Pages
12
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
9
Start Page
57692
End Page
57703
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/130157
DOI
10.1109/ACCESS.2021.3072653
ISSN
2169-3536
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.
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