Improved polytopic differential neural distinguishers for SIMON, SIMECK, and SPECK block ciphers

Abstract In recent years, the application of deep learning in cryptanalysis has gained significant attention, particularly with the emergence of neural network-based distinguishers. At CRYPTO’19, Gohr demonstrated that neural networks could develop differential distinguishers capable of producing highly competitive attacks against existing methods. Building on this foundation, we propose multiple input polytopic differential neural distinguishers ( PDND s) for the lightweight block ciphers SIMON, SIMECK, and SPECK. Our approach incorporates a novel data generation method that utilizes two polytope differences, resulting in more precise training data and enhanced model accuracy. Through extensive experiments in single-key and related-key scenarios, we evaluate and validate the intrinsic performance of our neural distinguishers. Our results show that PDND s significantly outperform the baseline, polytopic, multiple, and mixture differential neural distinguishers, utilizing a single input difference, in accuracy across various cipher rounds. Notably, our