Paper 2023/235
New Results on Machine Learning Based Distinguishers
, Nanyang Technological University, Silicon Austria Labs, Slovak University of Technology in Bratislava, Hansung UniversityAbstract
Machine Learning (ML) is almost ubiquitously used in multiple disciplines nowadays. Recently, we have seen its usage in the realm of differential distinguishers for symmetric key ciphers. In this work, we explore the possibility of a number of ciphers with respect to various ML-based distinguishers. We show new distinguishers on the unkeyed and round reduced version of SPECK-32, SPECK-128, ASCON, SIMECK-32, SIMECK-64 and SKINNY-128. We explore multiple avenues in the process. In summary, we use neural network as well as support vector machine in various settings (such as varying the activation function), apart from experimenting with a number of input difference tuples. Among other results, we show a distinguisher of 8-round SPECK-32 that works with practical data complexity (most of the experiments take a few hours on a personal computer).
Metadata
- Available format(s)
-
PDF
- Category
- Secret-key cryptography
- Publication info
- Published elsewhere. IEEE Access
- DOI
- 10.1109/ACCESS.2023.3270396
- Keywords
- speckasconsimeckskinnydistinguishermachine learningdifferential
- Contact author(s)
-
anubhab baksi @ ntu edu sg
jbreier @ jbreier com
vishnu98dasu @ gmail com
xiaolu hou @ stuba sk
khj1594012 @ gmail com
hwajeong84 @ gmail com - History
- 2023-04-29: last of 8 revisions
- 2023-02-20: received
- See all versions
- Short URL
- https://ia.cr/2023/235
- License
-
CC BY-NC-SA
BibTeX
@misc{cryptoeprint:2023/235,
author = {Anubhab Baksi and Jakub Breier and Vishnu Asutosh Dasu and Xiaolu Hou and Hyunji Kim and Hwajeong Seo},
title = {New Results on Machine Learning Based Distinguishers},
howpublished = {Cryptology ePrint Archive, Paper 2023/235},
year = {2023},
doi = {10.1109/ACCESS.2023.3270396},
note = {\url{https://eprint.iacr.org/2023/235}},
url = {https://eprint.iacr.org/2023/235}
}
