Welcome to the resource topic for 2022/1195
Title:
A Deep Neural Differential Distinguisher for ARX based Block Cipher
Authors: Debranjan Pal, Upasana Mandal, Mainak Chaudhury, Abhijit Das, Dipanwita Roy Chowdhury
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 cryptanalysis more easily.
In this paper, we report a generic tool using deep neural classifier that
assists to find differential distinguishers for block ciphers with reduced
round. We apply this approach for the differential cryptanalysis of ARX-
based encryption schemes HIGHT, LEA, and SPARX. The result shows
that our deep learning based distinguishers work with high accuracy for
14-round HIGHT, 13-Round LEA and 11-round SPARX. We also achieve
an improvement of the lower bound of data complexity for these three
ARX based ciphers.
ePrint: https://eprint.iacr.org/2022/1195
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