Paper 2020/846 Original: eprint.iacr.org/2020/846 ↗
Deep Learning based Differential Distinguisher for Lightweight Cipher PRESENT
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.
Metadata
- Available format(s)
PDF- Category
- Secret-key cryptography
- Publication info
- Preprint.
- History
- 2020-07-12: last updated
- 2020-07-12: published
- Short URL
- https://ia.cr/2020/846
- License
- https://creativecommons.org/licenses/by/4.0/
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}
}