SIMON和SIMECK分组密码的改进的（相关密钥）差分神经鉴别器
Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers
摘要：
Gohr在2019年国际密码学大会上进行了开创性的尝试，成功地将深度学习应用于NSA分组密码SPECK 32/64的差分密码分析中，实现了比纯差分密码分析更高的精度。从本质上讲，挖掘数据中的有效特征在数据驱动的深度学习中起着至关重要的作用。本文除了考虑密文对训练数据信息的完整性外，还将差分密码分析结构的领域知识考虑到深度学习的训练过程中，以提高性能。同时，以SIMON 32/64差分神经网络的性能为切入点，研究了输入差分对混合神经网络性能的影响，以选择合适的输入差分。最终，我们提高了SIMON 32/64，SIMON 64/128，SIMEK 32/64和SIMEK 64/128的神经网络的准确性。我们还首次在SIMON 32/64、SIMON 64/128、SIMEK 32/64和SIMEK 64/128的轮缩减版本上获得了基于相关关键差分的神经网络。
英文摘要：
In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of differential cryptanalysis is also considered into the training process of deep learning to improve the performance. Meanwhile, taking the performance of the differential-neural distinguisher of SIMON32/64 as an entry point, we investigate the impact of input difference on the performance of the hybrid distinguishers to choose the proper input difference. Eventually, we improve the accuracy of the neural distinguishers of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128. We also obtain related-key differential-based neural distinguishers on round-reduced versions of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128 for the first time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
