Research on multi-level container security isolation operation strategy based on static game.

With the increasing pressure on rail transit passenger services, the introduction of cloud-edge collaborative computing technology into passenger service systems has emerged as a research hotspot in recent years. The key issue in applying cloud computing to passenger service systems lies in enhancing the security and reliability of applications/data within containers in cloud-edge computing environments. Since passenger service systems operate in open network environments, they are exposed to numerous security vulnerabilities and malicious cyber-attacks, potentially leading to system crashes or the leakage of critical data. To mitigate the severe impacts of such attacks, effective container security isolation policies must be implemented. Firstly, a multi-level container security isolation model is proposed, along with the design of rules for configuring model security policies. Subsequently, a static game model is utilized to dynamically adjust optimal security strategies. By integrating the wolf pack algorithm with the co-evolution algorithm, a wolf pack-co-evolution algorithm is devised to ascertain the optimal solution for the static game, thereby determining the optimal security strategy. Finally, simulation experiments demonstrate that the wolf pack-co-evolution algorithm can effectively solve for the Nash equilibrium in the multi-level container security isolation static game model, enabling dynamic adjustments to be made to security strategies. This approach ensures the security of both container subjects and objects while enhancing container computing efficiency.