Paper 2025/537

Improved Framework of Related-key Differential Neural Distinguisher and Applications to the Standard Ciphers

Abstract

In recent years, the integration of deep learning with differential cryptanalysis has led to differential neural cryptanalysis, enabling efficient data-driven security evaluation of modern cryptographic algorithms. Compared to traditional differential cryptanalysis, differential neural cryptanalysis enhances the efficiency and automation of the analysis by training neural networks to automatically extract statistical features from ciphertext pairs. As research advances, neural distinguisher construction faces challenges due to the absence of a unified framework capable of cross-algorithm generalization and feature optimization. There's no systematic way to build a framework from data formats and network architectures, which limits their scalability across diverse ciphers and and their suitability for combining different cryptanalysis methods. While neural network training is data-driven, we lack interpretable explanations for the quality of differentially generated datasets. Therefore, there is an urgent need to combine cryptographic theory with data analysis methods to systematically evaluate dataset quality. This paper proposes a novel framework for constructing related-key neural differential distinguishers that integrates three core innovations: (1) multi-ciphertext multi-difference formats to enhance dataset diversity and feature coverage, (2) structural filtering for prioritizing high-probability differential paths aligned with cryptographic architectures, and (3) Deep Residual Shrinkage Network (DRSN) with adaptive thresholding to suppress noise and amplify critical differential features. By applying this framework to two standardized algorithms DES and PRESENT, our results demonstrate significant advancements. For DES, the framework achieves an 8-round related-key neural distinguisher and improves 6/7-round distinguisher accuracy by over 40%. For PRESENT, we construct the first 9-round related-key neural distinguisher, which outperforms existing neutral distinguishers in both round coverage and accuracy. Additionally, we employ kernel principal component analysis (KPCA) and K-means clustering to evaluate the quality of differential datasets for DES and PRESENT, revealing that clustering compactness strongly correlates with distinguisher performance. Furthermore, we propose a validation algorithm to verify differential combinations with cryptographic advantages from a machine learning perspective, identifying ‘good’ plaintext-key differential combinations. We apply this approach to the SIMECK algorithm, demonstrating its broad applicability. These findings validate the framework’s effectiveness in bridging cryptographic analysis with data-driven feature extraction and offer new insights for automated security evaluation of block ciphers.