New Results on Machine Learning Based Distinguishers

Anubhab Baksi, Jakub Breier, Vishnu Asutosh Dasu, Xiaolu Hou, Hyunji Kim, Hwajeong Seo

Abstract: Machine Learning (ML) is almost ubiquitously used in multiple disciplines nowadays. Recently, we have seen its usage in the realm of differential distinguishers for symmetric key ciphers. In this work, we explore the possibility of a number of ciphers with respect to various ML-based distinguishers. We show new distinguishers on the unkeyed and round reduced version of SPECK-32, SPECK-128, ASCON, SIMECK-32, SIMECK-64 and SKINNY-128. We explore multiple avenues in the process. In summary, we use neural network as well as support vector machine in various settings (such as varying the activation function), apart from experimenting with a number of input difference tuples. Among other results, we show a distinguisher of 8-round SPECK-32 that works with practical data complexity (most of the experiments take a few hours on a personal computer).