Paper 2020/913

Differential-ML Distinguisher: Machine Learning based Generic Extension for Differential Cryptanalysis

Tarun Yadav and Manoj Kumar

Abstract

Differential cryptanalysis is an important technique to evaluate the security of block ciphers. There exists several generalisations of differential cryptanalysis and it is also used in combination with other cryptanalysis techniques to improve the attack complexity. Usefulness of Machine learning in differential cryptanalysis is introduced by Gohr in 2019 to attack the lightweight block cipher SPECK. In this paper, we present a framework to combine the classical differential distinguisher and machine learning (ML) based differential distinguisher. We propose a novel technique to construct differential-ML distinguisher which provides better results with reduced data complexity. This technique is demonstrated on lightweight block ciphers SPECK & SIMON where 96% & 99% (or more) success rate is achieved for distinguishing the 6-round SPECK and 9-round SIMON respectively.