Security Analysis of SIMON32/64 Based on Deep Learning

被引：0
|
作者：
Wang H. [1 ]
Cong P. [1 ]
Jiang H. [1 ]
Wei Y. [1 ]
机构：
[1] Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2021年 / 58卷 / 05期
关键词：
Candidate key sieving; Deep learning; Differential cryptanalysis; Distinguisher; SIMON32/64;
D O I：
10.7544/issn1000-1239.2021.20200900
中图分类号：
学科分类号：
摘要：
With the rapid development of the Internet of Things, lightweight block cipher provides a solid foundation for the data security in various resource constrained environments. Currently, the security analysis of lightweight block ciphers tends to be more and more automated and intelligent. Applying deep learning to analyze the security of lightweight block ciphers appears to be a new research hotspot in this area. In this paper, the neural network technology is used to the security analysis of SIMON32/64, a lightweight block cipher algorithm released by the National Security Agency (NSA) in 2013. The feedforward neural network and the convolutional neural network are used to simulate the case of single input differential to multi output differential in multi differential cryptanalysis. Some deep learning distinguishers of 6-round (or even 9-round) reduced SIMON32/64 are designed, and both the advantages and disadvantages of the two neural network structures under different conditions are investigated. A candidate key sieving method for the 9-round reduced SIMON32/64 is also presented by extending the 7-round distinguisher of the feed-forward and the convolution neural networks, where one round forward and one round backward of this 7-round distinguisher are respectively considered. The experimental results show that 65535 candidate keys were dramatically reduced to 675 by only using 128 chosen plaintext pairs. Compared with the traditional differential distinguishers of reduced SIMON32/64, the new distinguishers combined with deep learning notably reduce both the time complexity and data complexity. © 2021, Science Press. All right reserved.
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页码：1056 / 1064
页数：8
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