IEICE TRANSACTIONS on Information
Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.7 pp.1240-1243
- Publication Date
- 2023/07/01
- Publicized
- 2023/04/13
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDL8094
- Type of Manuscript
- LETTER
- Category
- Information Network
Authors
Liu ZHANG
Xidian University
Zilong WANG
Xidian University
Yindong CHEN
Shantou University
Keyword
Latest Issue
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Liu ZHANG, Zilong WANG, Yindong CHEN, "Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 7, pp. 1240-1243, July 2023, doi: 10.1587/transinf.2022EDL8094.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8094/_p
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@ARTICLE{e106-d_7_1240,
author={Liu ZHANG, Zilong WANG, Yindong CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT},
year={2023},
volume={E106-D},
number={7},
pages={1240-1243},
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.},
keywords={},
doi={10.1587/transinf.2022EDL8094},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT
T2 - IEICE TRANSACTIONS on Information
SP - 1240
EP - 1243
AU - Liu ZHANG
AU - Zilong WANG
AU - Yindong CHEN
PY - 2023
DO - 10.1587/transinf.2022EDL8094
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2023
AB - 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.
ER -
English
