论文标题
改进的西蒙和Simeck块密码的基于差异的（相关）基于差异的神经区分器
Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers
论文作者
论文摘要
在Crypto 2019中，Gohr进行了开创性的尝试，并成功地将深度学习应用于针对NSA块密码Scip32/64的差异性加密分析，比纯差分区的差异差异更高。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。在本文中，除了考虑了密文对的训练数据中信息的完整性外，还考虑了有关差异隐式分析结构的领域知识，也被认为是深度学习以提高性能的训练过程。同时，以SIMON32/64的差分神经区分来表现为入口点，我们研究了输入差异对混合差异差的性能的影响，以选择适当的输入差异。最终，我们提高了SIMON32/64，SIMON64/128，SIMECK32/64和SIMECK64/128的神经区分器的准确性。我们还首次在Simon32/64，Simon64/128，Simeck32/64和Simeck64/128的圆头还原版本上获得了基于钥匙差异的神经区分。
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.
