A Novel Approach to Construct a Good Dataset for Differential-Neural Cryptanalysis

Recently, differential-neural cryptanalysis, which combines deep learning with differential cryptanalysis, has gained attention as a powerful and practical cryptanalysis method. This approach offers the advantage of enabling deep learning to analyze cryptographic properties, traditionally demanding substantial time and expertise. Nevertheless, the black-box nature of deep learning models poses challenges for cryptanalysts in comprehending the construction of the differential dataset. In particular, since the differential dataset serves as the foundation for generating the neural distinguisher, there is a pressing need for an effective methodology to construct a high-quality differential dataset. In this paper, we propose a novel approach to construct a good differential dataset for differential-neural cryptanalysis. First, we conducted an analysis to find the difference between well-trainable differential datasets and other differential datasets using Principal Component Analysis (PCA) and K-means clustering. Building upon our analysis results, we proposed the exploring algorithm for the generation of well-trainable differential dataset. This proposed algorithm assesses the input differences within the differential dataset by identifying significant principal components. If such components are found, a cluster evaluation is performed on the differential dataset. In our experiments, the proposed algorithm successfully identified favorable input differences, leading to improved accuracy in neural distinguisher training for SPECK and SIMON. Compared to the performance of the existing Gohr's neural input difference algorithm, our proposed algorithm was more effective in finding good input differences with higher accuracy. From the perspective of execution time, it showed an improvement of approximately 30%.