Paper 2025/697

A Multi-Differential Approach to Enhance Related-Key Neural Distinguishers

Abstract

At CRYPTO 2019, Gohr pioneered the integration of differential cryptanalysis with neural networks, demonstrating significant advantages over traditional distinguishers. Subsequently, at Inscrypt 2020, Su et al. proposed the concept of constructing polyhedral differential neural distinguishers by leveraging multiple effective input differences. More recently, at FSE 2024, Bellini et al. introduced a general-purpose tool for automating the training of single-key differential neural distinguishers for various block ciphers. Inspired by this body of work, we aim to extend automated search techniques to related-key differential neural distinguishers, enabling the discovery of effective input differences and key differences for such distinguishers. To achieve this, we employ a genetic optimization algorithm to identify effective differential combinations. To validate the efficacy of our method, we apply it to the Simeck and Simon cipher families, successfully identifying effective differential combinations for the three variants of Simeck and ten variants of Simon. Furthermore, inspired by the concept of polyhedral neural distinguishers, we adopt a novel data format that leverages multiple distinct input differences and key differences to construct positive and negative samples, providing the neural network with a richer set of features. Our approach not only identify high-quality distinguishers for previously unexplored cipher variants but also achieve higher accuracy for related-key differential neural distinguishers compared to the state-of-the-art.