Mixture Differential Cryptanalysis on Round-Reduced SIMON32/64 Using Machine Learning

Zehan Wu, Kexin Qiao (), Zhaoyang Wang, Junjie Cheng and Liehuang Zhu
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Zehan Wu: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Kexin Qiao: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Zhaoyang Wang: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Junjie Cheng: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Liehuang Zhu: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Mathematics, 2024, vol. 12, issue 9, 1-18

Abstract: With the development of artificial intelligence (AI), deep learning is widely used in various industries. At CRYPTO 2019, researchers used deep learning to analyze the block cipher for the first time and constructed a differential neural network distinguisher to meet a certain accuracy. In this paper, a mixture differential neural network distinguisher using ResNet is proposed to further improve the accuracy by exploring the mixture differential properties. Experiments are conducted on SIMON32/64, and the accuracy of the 8-round mixture differential neural network distinguisher is improved from 74.7% to 92.3%, compared with that of the previous differential neural network distinguisher. The prediction accuracy of the differential neural network distinguisher is susceptible to the choice of the specified input differentials, whereas the mixture differential neural network distinguisher is less affected by the input difference and has greater robustness. Furthermore, by combining the probabilistic expansion of rounds and the neutral bit, the obtained mixture differential neural network distinguisher is extended to 11 rounds, which can realize the 12-round actual key recovery attack on SIMON32/64. With an appropriate increase in the time complexity and data complexity, the key recovery accuracy of the mixture differential neural network distinguisher can be improved to 55% as compared to 52% of the differential neural network distinguisher. The mixture differential neural network distinguisher proposed in this paper can also be applied to other lightweight block ciphers.

Keywords: SIMON; ResNet; mixture differential; cryptanalysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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