Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (9): 44-51.doi: 10.11896/jsjkx.230600013

• Data Security • Previous Articles     Next Articles

Network Protocol Vulnerability Mining Method Based on the Combination of Generative AdversarialNetwork and Mutation Strategy

ZHUANG Yuan1, CAO Wenfang1, SUN Guokai1, SUN Jianguo2, SHEN Linshan1, YOU Yang3, WANG Xiaopeng3, ZHANG Yunhai3   

  1. 1 College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
    2 Hangzhou Institute of Technology,Xidian University,Hangzhou 311231,China
    3 NSFOCUS Technologies Group Co.,Ltd.,Beijing 100089,China
  • Received:2023-05-31 Revised:2023-07-22 Online:2023-09-15 Published:2023-09-01
  • About author:ZHUANG Yuan,born in 1988,Ph.D,lecturer,associate professor,master's supervisor.Her main research interests include blockchain security,machine learning,big data processing and distributed computing.
    SHENLinshan,born in 1978,master,associate professor,master's supervisor.His main research interests include industrial information security,machine learning and intelligent information processing.
  • Supported by:
    CCF-NSFOCUS(2021014),2022 Industrial Internet Innovation and Development Project－－Industrial Internet Data Security Detection Response and Traceability Project(TC220H055), Fundamental Research Funds for the Central Universities(3072022TS0604) and Concept Foundation of Hangzhou Institute of Technology,Xidian University(XJ2023230024).

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Abstract: With the deep integration of informatization and industrialization,the security issues of industrial Internet of things(IIoT) network protocols are becoming increasingly prominent.Existing network protocol vulnerability mining techniques mainly relyon feature variation and fuzzy testing,which have the limitations of depending on expert experience and cannot overcome the challenges posed by unknown protocols.To address the vulnerability mining challenges in IIoT protocols,this paper conducts research on the automation analysis and generation of vulnerability detection rules and proposes a network protocol vulnerability mining method based on a combination of generative adversarial networks(GANs) and mutation strategies.Firstly,a network protocol analysis model based on GANs is employed to conduct deep information mining on message sequences,extract message formats,and related features,enabling the recognition of network protocol structures.Then,by combining a guided iterative mutation strategy with a mutation operator library,directed test case generation rules are constructed to reduce the time for vulnerabi-lity discovery.Ultimately,an automated vulnerability mining method for unknown industrial control network protocols is deve-loped to meet the demand for protocol automated vulnerability mining in the existing industrial control application domain.Based on the above-mentioned approach,we conduct tests on two industrial control protocols(Modbus-TCP and S7) and evaluate them in terms of test coverage,vulnerability detection capability,test case generation time,and diversity.Experimental results show that the proposed method achieves a remarkable 89.4％ on the TA index.The AD index,which measures the ability to detect vu-lnerabilities in the simulated ModbusSlave system,reaches 6.87％.Additionally,the proposed method significantly reduces the time required for generating effective test cases,thereby enhancing the efficiency of industrial control protocol vulnerability discovery.

Key words: Generative adversarial network, Mutation strategy, Fuzzing test, Vulnerability mining, Network protocol

CLC Number: 

  • TP393
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