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. It has been shown that ML-based differential distinguishers can be easily extended to break round-reduced versions of ciphers. In this paper, we show new distinguishers on the unkeyed and round-reduced versions 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 networks and support vector machines 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 low data complexity.