Paper 2022/457

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

Liu Zhang
Zilong Wang
Abstract

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for Speck32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for Speck32/64 and Simon32/64. Inspired by Zhang’s work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher,that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.

Metadata
Available format(s)
PDF
Publication info
Preprint.
Keywords
Differential-Neural Distinguisher Inception Blocks DES Chaskey PRESENT
Contact author(s)
liuzhang @ stu xidian edu cn
zlwang @ xidian edu cn
History
2022-11-13: last of 2 revisions
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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