Paper 2021/310

A New Neural Distinguisher Considering Features Derived from Multiple Ciphertext Pairs

Yi Chen, Yantian Shen, Hongbo Yu, and Sitong Yuan

Abstract

Neural aided cryptanalysis is a challenging topic, in which the neural distinguisher (N D) is a core module. In this paper, we propose a new N D considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new N D to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new N D applies to two different neural aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc.

Note: This paper has been accepted for publication as a regular research paper. This is the final version.

Metadata
Available format(s)
PDF
Category
Secret-key cryptography
Publication info
Preprint. Minor revision.
Keywords
CryptanalysisNeural distinguisherDifferential cryptanalysisDeep learningData reuse
Contact author(s)
chenyi19 @ mails tsinghua edu cn
History
2022-02-24: last of 2 revisions
2021-03-09: received
See all versions
Short URL
https://ia.cr/2021/310
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2021/310,
      author = {Yi Chen and Yantian Shen and Hongbo Yu and Sitong Yuan},
      title = {A New Neural Distinguisher Considering Features Derived from Multiple Ciphertext Pairs},
      howpublished = {Cryptology ePrint Archive, Paper 2021/310},
      year = {2021},
      note = {\url{https://eprint.iacr.org/2021/310}},
      url = {https://eprint.iacr.org/2021/310}
}
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