Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (1): 371-381.doi: 10.11896/jsjkx.250300076

• Information Security • Previous Articles     Next Articles

Attack Graph-assisted Deep Reinforcement Learning-based Service Function Chain AttackRecovery Method

ZHOU Deqiang1, JI Xinsheng1,2, YOU Wei1, QIU Hang1 , YANG Jie1   

  1. 1 Institute of Information Technology, Information Engineering University, Zhengzhou 450002, China;
    2 Purple Mountain Laboratories, Nanjing 210000, China
  • Received:2025-03-14 Revised:2025-05-22 Published:2026-01-08
  • About author:ZHOU Deqiang,born in 1998,postgra-duate.His main research interests include 5G/6G network,network slicing and cyberspace security.
    JI Xinsheng,born in 1968,Ph.D,professor.His main research interests include next-generation mobile communication,network architecture and cyberspace security.
  • Supported by:
    National Key Research and Development Program of China(2022YFB2902204), Key Research and Development Project of Henan Province(231111211000) and Top Talent Training Project of Henan Province(244500510012).

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Abstract: SFC can provide customized services for the six scenarios of 6G with the advantages of on-demand orchestration,flexible networking,and other benefits,and 6G networks also put forward higher requirements for SFC.Resilience is receiving attention for the first time in 6G networks,requiring SFC to ensure stable and continuous service provision of fundamental function,with resilience recovery being a key stage.Existing recovery methods are often based on backup mechanisms leading to resource wastage,while ignoring the impact of network attack characteristics on recovery leading to difficulty in guaranteeing the recovery effect.Considering the characteristics of network attacks,this paper uses SFC attack graph to determine the customized attack recovery scheme for SFC,including the VNF recovery range and the demand of attack recovery level.To solve the placement scheme that conforms to the customized attack recovery scheme,a deep reinforcement learning-based SFC attack recovery method(DRL-SFCAR) is proposed.Extensive simulation results show that DRL-SFCAR performs better in terms of delay and recovery cost than the three comparison methods while ensuring recovery success rate.DRL-SFCAR can meet the attack recovery level requirement and minimize the long-term recovery cost,which achieves the customized recovery for SFC in network attack scenarios.

Key words: Service function chain, Resilience recovery, Attack graph, Deep reinforcing learning, Cost

CLC Number: 

  • TN915
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