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

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.