Higher Order Leakage Assessment and Neural Network-based Attack on CRYSTALS-Kyber

Buvana Ganesh, Mosabbah Ahmed, Alieeldin Mady

2024

Abstract

To enable the secure deployment of CRYSTALS-Kyber as the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standard for key encapsulation mechanisms (KEM), several attacks have emerged for both the algorithm and its implementations. In this work, a thorough higher order test vector leakage assessment has been performed on open source implementations of CRYSTALS-Kyber. With the traces obtained using the ChipWhisperer framework, the leakage is determined and a template Side Channel Attacks (SCA) is performed with deep learning to successfully uncover the secret key from the first-order masked implementation of CRYSTALS-Kyber. Overall, this work performs a comprehensive leakage assessment and neural network-based SCAs on the masked implementation of CRYSTALS-Kyber.

Paper Citation

in Harvard Style

Ganesh B., Ahmed M. and Mady A. (2024). Higher Order Leakage Assessment and Neural Network-based Attack on CRYSTALS-Kyber. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 373-380. DOI: 10.5220/0012715700003767

in Bibtex Style

@conference{secrypt24,
author={Buvana Ganesh and Mosabbah Ahmed and Alieeldin Mady},
title={Higher Order Leakage Assessment and Neural Network-based Attack on CRYSTALS-Kyber},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={373-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012715700003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Higher Order Leakage Assessment and Neural Network-based Attack on CRYSTALS-Kyber
SN - 978-989-758-709-2
AU - Ganesh B.
AU - Ahmed M.
AU - Mady A.
PY - 2024
SP - 373
EP - 380
DO - 10.5220/0012715700003767
PB - SciTePress