Paper 2024/2002

Improving Differential-Neural Distinguisher For Simeck Family

Abstract

In CRYPTO 2019, Gohr introduced the method of differential neural cryptanalysis, utilizing neural networks as the underlying distinguishers to achieve distinguishers for (5-8)-round of the Speck32/64 cipher and subsequently recovering keys for 11 and 12 rounds. Inspired by this work, we propose an enhanced neural cryptanalysis framework that combines the Efficient Channel Attention (ECA) module with residual networks. By introducing the channel attention mechanism to emphasize key features and leveraging residual networks to facilitate efficient feature extraction and gradient flow, we achieve improved performance. Additionally, we employ a new data format that combines the ciphertext and the penultimate round ciphertext as input samples, providing the distinguisher with more useful features. Compared with the known results, our work enhance the accuracy of the neural distinguishers for Simeck32/64 (10-12)-round and achieve a new 13-round distinguisher. We also improve the accuracy of the Simeck48/96 (10-11)-round distinguishers and develop new (12-16)-round neural distinguishers. Moreover, we enhance the accuracy of the Simeck64/128 (14-18)-round distinguishers and obtain a new 19-round neural distinguisher. As a result, we achieve the highest accuracy and the longest rounds distinguishers for Simeck32/64, Simeck48/96, and Simeck64/128.