Paper 2022/457

Improving Differential-Neural Distinguisher Model For DES, Chaskey and PRESENT

Liu Zhang and Zilong Wang

Abstract

In CRYPTO'19, Gohr proposed a new cryptanalysis strategy using machine learning algorithms. Combining the differential-neural distinguisher with a differential path and integrating the advanced key recovery procedure, Gohr achieved a 12-round key recovery attack on Speck32/64. Chen and Yu improved prediction accuracy of differential-neural distinguisher considering derived features from multiple-ciphertext pairs instead of single-ciphertext pairs. By modifying the kernel size of initial convolutional layer to capture more dimensional information, the prediction accuracy of differential-neural distinguisher can be improved for for three reduced symmetric ciphers. For DES, we improve the prediction accuracy of (5-6)-round differential-neural distinguisher and train a new 7-round differential-neural distinguisher. For Chaskey, we improve the prediction accuracy of (3-4)-round differential-neural distinguisher. For PRESENT, we improve the prediction accuracy of (6-7)-round differential-neural distinguisher. The source codes are available in https://drive.google.com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL?usp=sharing.

Metadata
Available format(s)
PDF
Publication info
Preprint. Minor revision.
Keywords
Differential-Neural DistinguisherInception BlocksDESChaskeyPRESENT
Contact author(s)
17lzhang3 @ gmail com
History
2022-04-14: revised
2022-04-12: received
See all versions
Short URL
https://ia.cr/2022/457
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2022/457,
      author = {Liu Zhang and Zilong Wang},
      title = {Improving Differential-Neural Distinguisher Model For DES, Chaskey and PRESENT},
      howpublished = {Cryptology ePrint Archive, Paper 2022/457},
      year = {2022},
      note = {\url{https://eprint.iacr.org/2022/457}},
      url = {https://eprint.iacr.org/2022/457}
}
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