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Paper 2021/310

A New Neural Distinguisher Considering Features Derived from Multiple Ciphertext Pairs

Yi Chen and Yantian Shen and Hongbo Yu and Sitong Yuan

Abstract

Neural aided cryptanalysis is a challenging topic, in which the neural distinguisher (ND) is a core module. In this paper, we propose a new ND considering multiple ciphertext pairs simultaneously. To our best knowledge, this is the only ND except for the ND proposed by Gohr at CRYPTO’19. Taking Gohr’s ND as the strong baseline model, we perform an in-depth analysis of our new ND. First, applications to five different ciphers show that our NDs achieve higher distinguishing accuracy. Second, we prove that our ND successfully captures features derived from multiple ciphertext pairs. Third, we further show how to perform various key recovery attacks with this new ND. More advantages of our ND are further discovered in key recovery attacks. Taking the neural aided statistical attack (NASA) as an example, we prove that the data complexity can be reduced by replacing Gohr’s ND with our ND.

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
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