Journal of Xidian University ›› 2024, Vol. 51 ›› Issue (1): 210-222.doi: 10.19665/j.issn1001-2400.20230212

• Cyberspace Security • Previous Articles    

Improvement of the neural distinguishers of several ciphers

YANG Xiaoxue1(), CHEN Jie1,2()   

  1. 1. School of Telecommunications Engineering,Xidian University,Xi’an 710071,China
    2. Henan Key Laboratory of Network Cryptography Technology,Zhengzhou 450001,China
  • Received:2022-12-06 Online:2023-08-22 Published:2023-08-22
  • Contact: CHEN Jie E-mail:1500020789@qq.com;jchen@mail.xidian.edu.cn

Abstract:

In order to further study the application of the neural network in cryptanalysis,the neural network differential divider of several typical lightweight block cipher algorithms is constructed and improved by using a deep residual network and traditional differential cryptanalysis techniques.The main results are as follows.First,the neural distinguishers of 4 to 7 rounds of PRESENT,3 rounds of KLEIN,7 to 9 rounds of LBlock and 7 to 10 rounds of Simeck32/64 are constructed and analyzed respectively based on the block cipher structure.Second,based on the characteristics of SPN structure block ciphers,PRESENT and KLEIN's neural distinguishers are improved,which can improve the accuracy of about 5.12% at most.In the study of LBlock’s neural distinguisher,it is verified that this improved method is not suitable for Feistel structure block ciphers.Third,based on the characteristics of the simeck 32/64 cryptography algorithm,the neural distinguisher is improved,with the accuracy improved by 2.3%.Meanwhile,the improved method of Simeck 32/64 is combined with the polyhedral difference analysis,and the accuracy of the existing 8-round and 9-round Simeck 32/64 poly neural network difference partition is increased by 1% and 3.2%.Finally,the three types of neural distinguishers obtained in the experiment are applied to the last round key recovery attack of 11-round simeck 32/64,with the best experimental result being a 99.4% success rate with 26.6 data complexity in 1 000 attacks.

Key words: neural differential distinguisher, lightweight block ciphers, partial key recovery attacks

CLC Number: 

  • TP309.7