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

Author:

Wu Zehan1,Qiao Kexin12ORCID,Wang  Zhaoyang1,Cheng  Junjie1,Zhu  Liehuang1

Affiliation:

1. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China

2. State Key Laboratory of Cryptology, P.O. Box 5159, Beijing 100878, China

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.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Open Project Fund of State Key Laboratory of Cryptology

Beijing Institute of Technology Research Fund Program for Young Scholars

Publisher

MDPI AG

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