Related-Key Neural Distinguisher for Round-Reduced PRESENT Cipher

Gohr presented benchmark work in CRYPTO 2019, and for the first time, deep learning was successfully applied to mount a cryptanalytic attack that was claimed to be better than its classical counterpart. He implemented deep learning-based differential cryptanalysis against the block cipher SPECK32/64. Lu et al. in 2022 built related-key neural distinguishers (RKNDs) against two lightweight ciphers, SIMON and SIMECK. They achieved significant accuracy for different number of rounds in both ciphers. In this paper, we construct a related-key neural distinguisher against ultra-lightweight block cipher PRESENT. The developed distinguisher provides some significant observations for the reduced number of rounds for PRESENT cipher.