Paper 2022/457
Improving Differential-Neural Distinguisher Model For DES, Chaskey and PRESENT
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)
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PDF
- Publication info
- Preprint.
- Keywords
- Differential-Neural Distinguisher Inception Blocks DES Chaskey PRESENT
- Contact author(s)
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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
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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},
url = {https://eprint.iacr.org/2022/457}
}
