Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (5): 118-124.doi: 10.13190/j.jbupt.2020-034

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Reverse-Analysis of S-Box for SM4-Like Algorithms Based on Side Channel Technology

MA Xiang-liang1,2, LI Bing3, YANG Dan3, HUANG Ke-zhen1,2, DUAN Xiao-yi4   

  1. 1. Trusted Computing and Information Assurance Laboratory, Institute of Software of Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. National Research Center for Information Technology Security, Beijing 100084, China;
    4. Department of Electronics and Information Engineering, Beijing Electronic Science and Technology Institute, Beijing 100070, China
  • Received:2020-03-31 Published:2021-03-11

Abstract: In the profiled scenario, the common method of reverse analysis is the template attack based on multi-Gaussian distribution. The article applies the concept of deep learning to the field of reverse analysis for the first time under the same conditions, and proposes an S-box reverse analysis algorithm based on deep learning. By selecting the deep learning algorithm, loss function and label design method suitable for side channel reverse analysis, an S-box reverse recovery experiment is conducted for SM4-like algorithm. It is shown that it is feasible to employ deep learning method to carry out S-box reverse analysis, which can have better performance comparing to template attack under certain circumstances. Moreover, the predicting effect of multi-layer perception algorithm surpasses that of convolutional neural network algorithm.

Key words: deep learning, reverse analysis, S box, SM4-like algorithm

CLC Number: