Paper 2023/461 Original: eprint.iacr.org/2023/461 ↗
Deep Learning based Differential Classifier of PRIDE and RC5
Abstract
Deep learning-based cryptanalysis is one of the emerging trends in recent times. Differential cryptanalysis is one of the most po- tent approaches to classical cryptanalysis. Researchers are now modeling classical differential cryptanalysis by applying deep learning-based tech- niques. In this paper, we report deep learning-based differential distin- guishers for block cipher PRIDE and RC5, utilizing deep learning models: CNN, LGBM and LSTM. We found distinguishers up to 23 rounds for PRIDE and nine rounds for RC5. To the best of our knowledge this is the first deep learning based differential classifier for cipher PRIDE and RC5.
Metadata
- Available format(s)
PDF- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- History
- 2023-03-30: last updated
- 2023-03-30: published
- Short URL
- https://ia.cr/2023/461
- License
- https://creativecommons.org/licenses/by-nc/4.0/
BibTeX
@misc{cryptoeprint:2023/461,
author = {Debranjan Pal and Upasana Mandal and Abhijit Das and Dipanwita Roy Chowdhury},
title = {Deep Learning based Differential Classifier of PRIDE and RC5},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/461},
year = {2023},
url = {https://eprint.iacr.org/2023/461}
}