Abstract:

With the generalized deployment of software defined network in the multi-domain environment of cloud-network convergence, the consistency verification of cross-domain defense strategies has become a key challenge in ensuring the quality and security of network services. The traditional full-path verification method is difficult to be applied to lightweight verification scenarios in large-scale cloud network environments due to its excessive computational and communication overheads and lack of dynamic adaptability. This paper proposed a lightweight verification method for cross-domain network defense strategies. Firstly, a hybrid algorithm based on deep reinforcement learning and optimal monitoring allocation was designed to dynamically and accurately locate the critical path and the minimum monitoring point set affected by the strategy, avoiding the distribution and detection across the entire network. Secondly, a multi-objective optimization task scheduling model was constructed. The verification priority was dynamically adjusted by comprehensively considering factors such as the urgency, importance of the strategy, and network load to achieve efficient utilization of verification resources. The experimental results show that, compared with the existing methods, the method proposed in this paper is suitable for large-scale dynamic cross-domain SDN environments.

Key words: cross-domain management, strategy verification, deep reinforcement learning, multi-objective optimization