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

Donggeun Kwon, Heeseok Kim, Seokhie Hong

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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 languageEnglish
Article number9400816
Pages (from-to)57692-57703
Number of pages12
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Autoencoder
  • cryptography
  • non-profiled
  • preprocessing
  • side-channel attacks

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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