Paper 2026/535

Improved Related-Key Differential Neural Distinguishers for SPN Block Ciphers

Abstract

Related-key differential neural distinguishers have recently attracted increasing attention in block-cipher cryptanalysis, yet their construction still relies heavily on cipher-specific manual design. In this paper, we study the systematic construction of related-key differential neural distinguishers for lightweight substitution–permutation network (SPN) block ciphers and propose a unified framework covering difference selection, dataset construction, network architecture, and training and evaluation. Within this framework, we develop a feature-enhancement method that exploits the invertibility of SPN components to derive representations more informative about the final-round internal state, and a sample-enhancement method that reuses each plaintext pair across related keys to derive multiple ciphertext-pair relations, thereby enriching each sample without increasing the plaintext budget. We validate the proposed framework on SKINNY-64/64 and PRESENT-64/80. Experimental results demonstrate that the proposed method can effectively construct multi-round related-key differential neural distinguishers, with accuracy improving consistently as the number of plaintext pairs per sample increases. In particular, for SKINNY-64/64, the single-pair setting achieves classification accuracies of 100.0%, 68.2%, and 59.3% for 7, 8, and 9 rounds, respectively, providing, to the best of our knowledge, the first experimental results on related-key differential neural distinguishers for this cipher. For PRESENT-64/80, under the four-pair setting, the proposed method achieves competitive distinguishing performance up to 9 rounds, with accuracies of 95.6%, 72.0%, and 53.7% for 7, 8, and 9 rounds, respectively.