ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (5): 1056-1064.doi: 10.7544/issn1000-1239.2021.20200900

Special Issue: 2021人工智能安全与隐私保护技术专题

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Security Analysis of SIMON32/64 Based on Deep Learning

Wang Huijiao, Cong Peng, Jiang Hua, Wei Yongzhuang   

  1. (Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin, Guangxi 541004)
  • Online:2021-05-01
  • Supported by: 
    This work was supported by the Guangxi Natural Science Foundation (2019GXNSFGA245004), the Science and Technology Major Project of Guangxi (Guike AA18118025), and the Project of Guangxi Key Laboratory of Trusted Software (KX202056).

Abstract: 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.

Key words: SIMON32/64, deep learning, differential cryptanalysis, distinguisher, candidate key sieving

CLC Number: