Paper 2022/886

Deep Learning based Cryptanalysis of Lightweight Block Ciphers, Revisited

Hyunji Kim, Hansung University
Sejin Lim, Hansung University
Yeajun Kang, Hansung University
Wonwoong Kim, Hansung University
Hwajeong Seo, Hansung University
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.
Keywords
Cryptanalysis Deep Learning Lightweight Block Ciphers
Contact author(s)
khj1594012 @ gmail com
dlatpwls834 @ gmail com
etus1211 @ gmail com
dnjsdndeee @ gmail com
hwajeong84 @ gmail com
History
2022-07-07: approved
2022-07-07: received
See all versions
Short URL
https://ia.cr/2022/886
License
No rights reserved
CC0

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},
      note = {\url{https://eprint.iacr.org/2022/886}},
      url = {https://eprint.iacr.org/2022/886}
}
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