Paper 2022/1195
A Deep Neural Differential Distinguisher for ARX based Block Cipher
Abstract
Over the last few years, deep learning is becoming the most trending topic for the classical cryptanalysis of block ciphers. Differential cryptanalysis is one of the primary and potent attacks on block ciphers. Here we apply deep learning techniques to model differential cryptanaly- sis more easily. In this paper, we report a generic tool called NDDT1, us- ing deep neural classifier that assists to find differential distinguishers for symmetric block ciphers with reduced round. We apply this approach for the differential cryptanalysis of ARX-based encryption schemes HIGHT, LEA, SPARX and SAND. To the best of our knowledge, this is the first deep learning-based distinguisher for the mentioned ciphers. The result shows that our deep learning based distinguishers work with high accuracy for 14-round HIGHT, 13-Round LEA, 11-round SPARX and 14-round SAND128. The relationship between the hamming weight of input difference of a neural distinguisher and the corresponding maxi- mum round number of the cipher has been justified through exhaustive experimentation. The lower bounds of data complexity for differential cryptanalysis have also been improved.
Metadata
- Available format(s)
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- HIGHTLEASPARXSANDNeural DistinguisherDeep LearningDifferential Cryptanalysis
- Contact author(s)
-
debranjan crl @ gmail com
mandal up98 @ gmail com
mainakcacsgu @ gmail com
abhij @ cse iitkgp ac in
drc @ cse iitkgp ac in - History
- 2023-09-12: revised
- 2022-09-10: received
- See all versions
- Short URL
- https://ia.cr/2022/1195
- License
-
CC BY-NC-SA
BibTeX
@misc{cryptoeprint:2022/1195, author = {Debranjan Pal and Upasana Mandal and Mainak Chaudhury and Abhijit Das and Dipanwita Roy Chowdhury}, title = {A Deep Neural Differential Distinguisher for {ARX} based Block Cipher}, howpublished = {Cryptology {ePrint} Archive, Paper 2022/1195}, year = {2022}, url = {https://eprint.iacr.org/2022/1195} }