Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (12): 118-124.doi: 10.11896/jsjkx.211200029

• Computer Software • Previous Articles     Next Articles

Software Diversity Evaluation Method Based on Multi-granularity Features

CHI Yu-ning, GUO Yun-fei, WANG Ya-wen, HU Hong-chao   

  1. Institution of Scientific and Technical Information,People’s Liberation Army Strategic Support Force Information Engineering University,Zhengzhou 450001,China
  • Received:2021-12-02 Revised:2022-05-16 Published:2022-12-14
  • About author:CHI Yu-ning,born in 1995,postgra-duate.Her main research interests include software diversification and mi-micry defense.WANG Ya-wen,born in 1991,Ph.D.His main research interests include cloud computing security and scientific workflow security.
  • Supported by:
    National Key Research and Development Program of China(2021YFB1006200,2021YFB1006201) and National Natural Science Foundation of China(62072467).

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Abstract: Aiming at the problem that existing software diversity evaluation methods generally adopt single feature,a software diversity evaluation method based on multi-granularity feature is proposed.This method analyzes four granularity of program:instruction,function,basic block and binary file.First,different granularity are obtained by small prime product method and dyna-mic weight distribution algorithm.Then,the granularity is analyzed according to the effectiveness of diversification technology.In the experimental part,GNU coreutils is used to comprehensively evaluate 7 software diversification methods.The result is analyzed to verify the applicability of the evaluation algorithm.Experimental results show that this evaluation method can accurately evaluate the effectiveness of software diversification methods from both vertical and horizontal directions,which has reference value for the research direction of subsequent diversification technology.

Key words: Software diversity, Multi-granularity feature, Diversity analysis, Prime product method, Quantitative evaluation

CLC Number: 

  • TP393
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