Traditional research studies on interdependent networks with groups ignore the relationship between nodes in dependency groups. In real-world networks, nodes in the same group may support each other through cooperation and tend to fail or survive together. In this paper, based on the framework of group percolation, a cascading failure model on interdependent networks with cooperative dependency groups under targeted attacks is proposed, and the effect of group size distributions on the robustness of interdependent networks is investigated. The mutually giant component and phase transition point of networks with different group size distributions are analyzed. The effectiveness of the theory is verified through simulations. Results show that the robustness of interdependent networks with cooperative dependency groups can be enhanced by increasing the heterogeneity between groups under targeted attacks. The theory can well predict the numerical simulation results. This model provides some theoretical guidance for designing robust interdependent systems in real world.
In order to enhance the isolation security of 5G cryptographic computing, a network slice deployment method for cryptographic computing isolation was proposed in this paper. Firstly, based on hardware cryptographic virtualization technology, the Network Function Virtualization (NFV) architecture based on cryptographic card virtualization was designed in this paper. By analyzing the characteristics of cryptographic calculation of different Virtual Network Function (VNF) requirement in 5G network slices, the allocation policies of cryptographic resources are set. Then, the deployment method was established as a mixed integer programming model, taking the deployment cost as the objective function, and reducing the deployment cost of network slice by minimizing the objective function. Finally, genetic algorithm is used to simulate the model. Experiments show that the proposed method reduces the deployment cost on the premise of ensuring security.
The robustness of interdependent networks has attracted much attention recently. Existing studies mainly focus on random failures, however, targeted attacks are ubiquitous in the real world. In this paper, a low degree neighbor node priority coupling (LDNPC) strategy is proposed, which enhances the robustness of interdependent networks under high-degree node targeted attacks by changing the relationship between layers to generate structures of overlapping links. First, nodes are ranked in descend according to their degrees. Then, nodes with low degrees have priority to choose their dependency partners to generate structures of overlapping links. Finally, experiments on three models of interdependent networks show that LDNPC can enhance the robustness of interdependent networks against high-degree node targeted attacks effectively.
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