Welcome to the resource topic for 2023/1100
Title:
Shift-invariance Robustness of Convolutional Neural Networks in Side-channel Analysis
Authors: Marina Krček, Lichao Wu, Guilherme Perin, Stjepan Picek
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 is commonly investigated in related works - 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 propose to use data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show the proposed techniques work very well and improve the attack significantly, even for an order of magnitude.
ePrint: https://eprint.iacr.org/2023/1100
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