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Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis

Author

Listed:
  • Marina Krček

    (Independent Researcher, 2624 Delft, The Netherlands)

  • Lichao Wu

    (System Security Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany)

  • Guilherme Perin

    (Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands)

  • Stjepan Picek

    (Digital Security Group, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands)

Abstract

Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure commonly investigated in related works is desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We investigate data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show that the proposed techniques work very well and improve the attack significantly, even for an order of magnitude.

Suggested Citation

  • Marina Krček & Lichao Wu & Guilherme Perin & Stjepan Picek, 2024. "Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis," Mathematics, MDPI, vol. 12(20), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3279-:d:1502059
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