Novel distinguisher for SM4 cipher algorithm based on deep learning
A method was proposed to construct a deep learning distinguisher model for large state block ciphers with large-block and long-key in view of the problem of high data complexity, time complexity and storage complexity of large state block cipher distinguishers, and the neural distinguishers were con...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023141/ |
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| author | Huijiao WANG Xin ZHANG Yongzhuang WEI Lingchen LI |
| author_facet | Huijiao WANG Xin ZHANG Yongzhuang WEI Lingchen LI |
| author_sort | Huijiao WANG |
| collection | DOAJ |
| description | A method was proposed to construct a deep learning distinguisher model for large state block ciphers with large-block and long-key in view of the problem of high data complexity, time complexity and storage complexity of large state block cipher distinguishers, and the neural distinguishers were constructed for SM4 algorithm.Drawing inspiration from the idea that ciphertext difference could improve the performance of distinguishers, a new input data format for neural distinguisher was designed by using partial difference information between ciphertext pairs as part of the training data.The residual neural network model was used to construct the neural distinguisher.The training dataset for large blocks was preprocessed.Additionally, an improved strategy for model relearning was proposed to address the high specificity and low sensitivity of the constructed distinguisher.Experimental results show that the proposed deep learning model for SM4 can achieve 9 rounds neural distinguisher.The accuracy of 4～9 rounds distinguishers can reach up to 100%, 76.14%, 65.20%, 59.28%, 55.89% and 53.73% respectively.The complexity and accuracy of the constructed differential neural distinguisher are significantly better than those of traditional differential distinguishers, and it is currently the best neural distinguisher for the block cipher SM4 to our knowledge.It also proves that the deep learning method is effective and feasible in the security analysis of block cipher of large block. |
| format | Article |
| id | doaj-art-339d7675f4cb4e31b538eab2e513dea4 |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-339d7675f4cb4e31b538eab2e513dea42025-08-20T02:40:47ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-07-014417118459384046Novel distinguisher for SM4 cipher algorithm based on deep learningHuijiao WANGXin ZHANGYongzhuang WEILingchen LIA method was proposed to construct a deep learning distinguisher model for large state block ciphers with large-block and long-key in view of the problem of high data complexity, time complexity and storage complexity of large state block cipher distinguishers, and the neural distinguishers were constructed for SM4 algorithm.Drawing inspiration from the idea that ciphertext difference could improve the performance of distinguishers, a new input data format for neural distinguisher was designed by using partial difference information between ciphertext pairs as part of the training data.The residual neural network model was used to construct the neural distinguisher.The training dataset for large blocks was preprocessed.Additionally, an improved strategy for model relearning was proposed to address the high specificity and low sensitivity of the constructed distinguisher.Experimental results show that the proposed deep learning model for SM4 can achieve 9 rounds neural distinguisher.The accuracy of 4～9 rounds distinguishers can reach up to 100%, 76.14%, 65.20%, 59.28%, 55.89% and 53.73% respectively.The complexity and accuracy of the constructed differential neural distinguisher are significantly better than those of traditional differential distinguishers, and it is currently the best neural distinguisher for the block cipher SM4 to our knowledge.It also proves that the deep learning method is effective and feasible in the security analysis of block cipher of large block.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023141/block cipherdeep learningneural distinguisherSM4 algorithmcomplexity |
| spellingShingle | Huijiao WANG Xin ZHANG Yongzhuang WEI Lingchen LI Novel distinguisher for SM4 cipher algorithm based on deep learning Tongxin xuebao block cipher deep learning neural distinguisher SM4 algorithm complexity |
| title | Novel distinguisher for SM4 cipher algorithm based on deep learning |
| title_full | Novel distinguisher for SM4 cipher algorithm based on deep learning |
| title_fullStr | Novel distinguisher for SM4 cipher algorithm based on deep learning |
| title_full_unstemmed | Novel distinguisher for SM4 cipher algorithm based on deep learning |
| title_short | Novel distinguisher for SM4 cipher algorithm based on deep learning |
| title_sort | novel distinguisher for sm4 cipher algorithm based on deep learning |
| topic | block cipher deep learning neural distinguisher SM4 algorithm complexity |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023141/ |
| work_keys_str_mv | AT huijiaowang noveldistinguisherforsm4cipheralgorithmbasedondeeplearning AT xinzhang noveldistinguisherforsm4cipheralgorithmbasedondeeplearning AT yongzhuangwei noveldistinguisherforsm4cipheralgorithmbasedondeeplearning AT lingchenli noveldistinguisherforsm4cipheralgorithmbasedondeeplearning |