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

Donggeun Kwon, Heeseok Kim, Seokhie Hong

    Research output: Contribution to journalArticlepeer-review

    28 Citations (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

    Bibliographical note

    Funding Information:
    This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under Grant NRF-2019R1A2C2088960.

    Publisher Copyright:
    © 2013 IEEE.

    Keywords

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

    ASJC Scopus subject areas

    • General Engineering
    • General Materials Science
    • General Computer Science

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