Paper 2021/719

Enhancing Differential-Neural Cryptanalysis

Zhenzhen Bao, Tsinghua University
Jian Guo, Nanyang Technological University
Meicheng Liu, Chinese Academy of Sciences
Li Ma, Chinese Academy of Sciences
Yi Tu, Nanyang Technological University

In CRYPTO 2019, Gohr shows that well-trained neural networks can perform cryptanalytic distinguishing tasks superior to traditional differential distinguishers. Moreover, applying an unorthodox key guessing strategy, an 11-round key-recovery attack on a modern block cipher Speck32/64 improves upon the published state-of-the-art result. This calls into the next questions. To what extent is the advantage of machine learning (ML) over traditional methods, and whether the advantage generally exists in the cryptanalysis of modern ciphers? To answer the first question, we devised ML-based key-recovery attacks on more extended round-reduced Speck32/64. We achieved an improved 12-round and the first practical 13-round attacks. The essential for the new results is enhancing a classical component in the ML-based attacks, that is, the neutral bits. To answer the second question, we produced various neural distinguishers on round-reduced Simon32/64 and provided comparisons with their pure differential-based counterparts.

Note: This manuscript is the full version of the conference version.

Available format(s)
Secret-key cryptography
Publication info
A major revision of an IACR publication in ASIACRYPT 2022
Differential Cryptanalysis Neural Distinguisher Key Recovery Simon Speck Generalized Neutral Bits Bayesian Search
Contact author(s)
zzbao @ tsinghua edu cn
guojian @ ntu edu sg
liumeicheng @ iie ac cn
skloismary @ gmail com
tuyi0002 @ e ntu edu sg
2022-09-21: last of 3 revisions
2021-05-31: received
See all versions
Short URL
Creative Commons Attribution


      author = {Zhenzhen Bao and Jian Guo and Meicheng Liu and Li Ma and Yi Tu},
      title = {Enhancing Differential-Neural Cryptanalysis},
      howpublished = {Cryptology ePrint Archive, Paper 2021/719},
      year = {2021},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.