Paper 2022/886 Original: eprint.iacr.org/2022/886 ↗
Deep Learning based Cryptanalysis of Lightweight Block Ciphers, Revisited
Abstract
Cryptanalysis is to infer the secret key of cryptography algorithm. There are brute-force attack, differential attack, linear attack, and chosen plaintext attack. With the development of artificial intelligence, deep learning-based cryptanalysis has been actively studied. There are works in which known-plaintext attacks against lightweight block ciphers, such as S-DES, have been performed. In this paper, we propose a cryptanalysis method based on the-state-of-art deep learning technologies (e.g. residual connections and gated linear units) for lightweight block ciphers (e.g. S-DES and S-AES). The number of parameters required for training is significantly reduced by 93.16~\% and the average of bit accuracy probability increased by about 5.3~\%, compared with previous work.
Metadata
- Available format(s)
PDF- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- History
- 2023-05-08: last updated
- 2022-07-07: published
- Short URL
- https://ia.cr/2022/886
- License
- https://creativecommons.org/publicdomain/zero/1.0/
BibTeX
@misc{cryptoeprint:2022/886,
author = {Hyunji Kim and Sejin Lim and Yeajun Kang and Wonwoong Kim and Hwajeong Seo},
title = {Deep Learning based Cryptanalysis of Lightweight Block Ciphers, Revisited},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/886},
year = {2022},
url = {https://eprint.iacr.org/2022/886}
}