Print Email Facebook Twitter Fake It Till You Make It Title Fake It Till You Make It: Data Augmentation Using Generative Adversarial Networks for All the Crypto You Need on Small Devices Author Mukhtar, Naila (Macquarie University) Batina, Lejla (Radboud Universiteit Nijmegen) Picek, S. (TU Delft Cyber Security; Radboud Universiteit Nijmegen) Kong, Yinan (Macquarie University) Contributor Galbraith, Steven D. (editor) Date 2022 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. Subject ASCADData augmentationDeep learning-based side-channel attacksElliptic curve cryptographyGANsSignal processing To reference this document use: http://resolver.tudelft.nl/uuid:833ceea0-d96a-4886-aefe-4a5bbc59d0df DOI https://doi.org/10.1007/978-3-030-95312-6_13 Publisher Springer, Cham Embargo date 2023-07-01 ISBN 978-3-030-95311-9 Source Topics in Cryptology - CT-RSA 2022: Cryptographers’ Track at the RSA Conference, 2022, Proceedings Event Cryptographers Track at the RSA Conference, CT-RSA 2022, 2022-03-01 → 2022-03-02, Virtual, Online Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 13161 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. Part of collection Institutional Repository Document type conference paper Rights © 2022 Naila Mukhtar, Lejla Batina, S. Picek, Yinan Kong Files PDF Mukhtar2022_Chapter_FakeI ... ugment.pdf 4.1 MB Close viewer /islandora/object/uuid:833ceea0-d96a-4886-aefe-4a5bbc59d0df/datastream/OBJ/view