Paper 2022/030

Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers

Jinyu Lu
Guoqiang Liu
Bing Sun
Chao Li
Li Liu

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.

Available format(s)
Secret-key cryptography
Publication info
Published elsewhere. The Computer Journal
Deep Learning(Related-key) Differential DistinguisherSimonSimeckInput Difference
Contact author(s)
jinyu_smile @ foxmail com
liuguoqiang87 @ hotmail com
happy_come @ 163 com
lichao_nudt @ sina com
li liu @ oulu fi
2022-12-30: revised
2022-01-14: received
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Creative Commons Attribution


      author = {Jinyu Lu and Guoqiang Liu and Bing Sun and Chao Li and Li Liu},
      title = {Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers},
      howpublished = {Cryptology ePrint Archive, Paper 2022/030},
      year = {2022},
      note = {\url{}},
      url = {}
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