Improved Neural Aided Statistical Attack for Cryptanalysis

Yi Chen; Hongbo Yu

Paper 2021/311

Improved Neural Aided Statistical Attack for Cryptanalysis

Yi Chen and Hongbo Yu

Abstract

At CRYPTO 2019, Gohr improved attacks on Speck32/64 using deep learning. In 2020, Chen et al. proposed a neural aided statistical attack that is more generic. Chen et’s attack is based on a statistical distinguisher that covers a prepended differential transition and a neural distinguisher. When the probability of the differential transition is pq, its impact on the data complexity is O(p^{-2}q^{-2}. In this paper, we propose an improved neural aided statistical attack based on a new concept named Homogeneous Set. Since partial random ciphertext pairs are filtered with the help of homogeneous sets, the differential transition’s impact on the data complexity is reduced to O(p^{−1}q^{−2}). As a demonstration, the improved neural aided statistical attack is applied to round-reduced Speck. And several better attacks are obtained.

Metadata

Available format(s): PDF
Category: Secret-key cryptography
Publication info: Preprint. MINOR revision.
Contact author(s): chenyi19 @ mails tsinghua edu cn
History: 2021-03-09: received
Short URL: https://ia.cr/2021/311
License: CC BY

BibTeX

@misc{cryptoeprint:2021/311,
      author = {Yi Chen and Hongbo Yu},
      title = {Improved Neural Aided Statistical Attack for Cryptanalysis},
      howpublished = {Cryptology ePrint Archive, Paper 2021/311},
      year = {2021},
      note = {\url{https://eprint.iacr.org/2021/311}},
      url = {https://eprint.iacr.org/2021/311}
}