Fake It Till You Make It: Data Augmentation Using Generative Adversarial Networks for All the Crypto You Need on Small Devices

Naila Mukhtar*, Lejla Batina, Stjepan Picek, Yinan Kong

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

Deep learning-based side-channel analysis performance heavily depends on the dataset size and the number of instances in each target class. Both small and imbalanced datasets might lead to unsuccessful side-channel attacks. The attack performance can be improved by generating traces synthetically from the obtained data instances instead of collecting them from the target device, but this is a cumbersome and challenging task. We propose a novel data augmentation approach based on conditional Generative Adversarial Networks (cGAN) and Siamese networks, enhancing the attack capability. We also present a quantitative comparative deep learning-based side-channel analysis between a real raw signal leakage dataset and an artificially augmented leakage dataset. The analysis is performed on the leakage datasets for both symmetric and public-key cryptographic implementations. We investigate non-convergent networks’ effect on the generation of fake leakage signals using two cGAN based deep learning models. The analysis shows that the proposed data augmentation model results in a well-converged network that generates realistic leakage traces, which can be used to mount deep learning-based side-channel analysis successfully even when the dataset available from the device is not optimal. Our results show that the datasets enhanced with “faked” leakage traces are breakable (while not without augmentation), which might change how we perform deep learning-based side-channel analysis.

Original languageEnglish
Title of host publicationTopics in Cryptology - CT-RSA 2022
Subtitle of host publicationCryptographers’ Track at the RSA Conference, 2022, Proceedings
EditorsSteven D. Galbraith
Place of PublicationCham
PublisherSpringer
Pages297-321
Number of pages25
ISBN (Electronic)978-3-030-95312-6
ISBN (Print) 978-3-030-95311-9
DOIs
Publication statusPublished - 2022
EventCryptographers Track at the RSA Conference, CT-RSA 2022 - Virtual, Online
Duration: 1 Mar 20222 Mar 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume13161
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceCryptographers Track at the RSA Conference, CT-RSA 2022
CityVirtual, Online
Period1/03/222/03/22

Bibliographical note

Accepted author manuscript

Keywords

  • ASCAD
  • Data augmentation
  • Deep learning-based side-channel attacks
  • Elliptic curve cryptography
  • GANs
  • Signal processing

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