The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
No CRYPTO 2019, Gohr introduziu pela primeira vez o método de aprendizagem profunda para criptoanálise para SPEQUE32/64. Um diferenciador neural diferencial foi obtido usando a rede neural ResNet. Zhang et ai. usou múltiplas camadas convolucionais paralelas com diferentes tamanhos de kernel para capturar informações de múltiplas dimensões, melhorando assim a precisão ou obtendo um maior número de distinções para SPEQUE32/64 e SESTOU DENTRO32/64. Inspirados no trabalho de Zhang, aplicamos a estrutura de rede a outras cifras. Não apenas melhoramos a precisão do diferenciador, mas também aumentamos o número de rodadas do diferenciador, ou seja, distinguimos mais rodadas de texto cifrado e número aleatório para DES, Chaskey e PRESENT.
Liu ZHANG
Xidian University
Zilong WANG
Xidian University
Yindong CHEN
Shantou University
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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},}
Copiar
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 -