Paper 2020/846
Deep Learning based Differential Distinguisher for Lightweight Cipher PRESENT
Aayush Jain, Varun Kohli, and Girish Mishra
Abstract
Recent years have seen a major involvement of deep learning architecture in the cryptanalysis of various lightweight ciphers. The present study is inspired by the work of Gohr and Baksi et al. in the field to develop a deep neural network-based differential distinguisher for round reduced PRESENT lightweight block cipher. We present a multi-layer perceptron network which can distinguish between 3-6 rounds of PRESENT cipher data and a randomly generated data with a significantly high probability. We also discuss the possible improvements in the original approach of the differential distinguisher presented by Baksi et al.
Note: We have developed on the previous works by Baksi et. al. and Gohr on Deep Learning based Differential Distinguishers. This paper contains the results obtained so far.
Metadata
- Available format(s)
-
PDF
- Category
- Secret-key cryptography
- Publication info
- Preprint. MINOR revision.
- Keywords
- Block CiphersCryptanalysisDeep LearningDifferential DistinguisherPRESENT
- Contact author(s)
-
varunkohli2013 @ gmail com
aayushjain0829 @ gmail com
gmishratech28 @ gmail com - History
- 2020-07-12: received
- Short URL
- https://ia.cr/2020/846
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/846,
author = {Aayush Jain and Varun Kohli and Girish Mishra},
title = {Deep Learning based Differential Distinguisher for Lightweight Cipher {PRESENT}},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/846},
year = {2020},
url = {https://eprint.iacr.org/2020/846}
}
