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

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 desynchr...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2024-10, Vol.12 (20), p.3279
Main Authors: Krček, Marina, Wu, Lichao, Perin, Guilherme, Picek, Stjepan
Format: Article
Language:English
Subjects:
Artificial neural networks
countermeasures
Data augmentation
Data security
Datasets
Deep learning
Expected values
Invariance
Machine learning
Methods
Misalignment
Neural networks
shift-invariance
side-channel analysis
Target masking
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12203279