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

3 Citations (Scopus)
61 Downloads (Pure)


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
Number of pages25
ISBN (Electronic)978-3-030-95312-6
ISBN (Print) 978-3-030-95311-9
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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceCryptographers Track at the RSA Conference, CT-RSA 2022
CityVirtual, Online

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


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


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