Cryptology ePrint Archive: Report 2020/846

Deep Learning based Differential Distinguisher for Lightweight Cipher PRESENT

Aayush Jain and 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.

Category / Keywords: secret-key cryptography / Block Ciphers, Cryptanalysis, Deep Learning, Differential Distinguisher, PRESENT

Date: received 8 Jul 2020

Contact author: varunkohli2013 at gmail com,aayushjain0829@gmail com,gmishratech28@gmail com

Available format(s): PDF | BibTeX Citation

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.

Version: 20200712:123739 (All versions of this report)

Short URL: ia.cr/2020/846


[ Cryptology ePrint archive ]