The Internet creates multidimensional and complex relationships in terms of the composition, application and mapping of social users. Most of the previous related research has focused on the single-layer topology of physical device networks but ignored the study of service access relationships and the social structure of users on the Internet. Here, we propose a composite framework to understand how the interaction between the physical devices network, business application network, and user role network affects the robustness of the entire Internet. In this paper, a multilayer network consisting of a physical device layer, business application layer and user role layer is constructed by collecting experimental network data. We characterize the disturbance process of the entire multilayer network when a physical entity device fails by designing nodal disturbance to investigate the interactions that exist between the different network layers. Meanwhile, we analyze the characteristics of the Internet-oriented multilayer network structure and propose a heuristic multilayer network topology generation algorithm based on the initial routing topology and networking pattern, which simulates the evolution process of multilayer network topology. To further analyze the robustness of this multilayer network model, we combined a total of six target node ranking indicators including random strategy, degree centrality, betweenness centrality, closeness centrality, clustering coefficient and network constraint coefficient, performed node deletion simulations in the experimental network, and analyzed the impact of component types and interactions on the robustness of the overall multilayer network based on the maximum component change in the network. These results provide new insights into the operational processes of the Internet from a multi-domain data fusion perspective, reflecting that the coupling relationships that exist between the different interaction layers are closely linked to the robustness of multilayer networks.

The research goal of cyberspace security situational awareness analysis is to predict the future security development of the target network by acquiring, understanding, and displaying the security elements in the large-scale network environment. Current cyberspace security situational awareness systems are mostly based on traditional single-layer network topology to analyze the security of the target network's operational posture. However, as the scale of the network continues to expand, the network structure becomes more complex, and the information fusion in multiple fields in practical applications deepens, the single-layer topology model can no longer meet the analysis requirements. In this paper, we construct a multilayer network topology model for cyberspace security situational awareness by integrating multidimensional information in the physical device layer network, business application layer network, and user role layer network. Meanwhile, to eliminate the limitations of traditional node importance indicators, a node importance assessment indicator that integrates topological centrality and node dependency factor is proposed in conjunction with model characteristics: multilayer dependency CRITIC indicator ( MDCI ). On the one hand, MDCI fits a variety of evaluation metrics through the CRITIC multi-attribute decision method to comprehensively assess the importance of nodes in network centrality, and on the other hand, MDCI better aggregates the important contributions of nodes in each network layer based on node dependency factor to coordinate multilayer network information. The experimental results show that MDCI has better ordering monotonicity and generates more stable metric sequences, and can effectively cause large-scale failures in multilayer network while destroying fewer physical device components, which can be better adapted to the critical node identification needs of multilayer network.

In large-scale network topology discovery, due to the complex network structure and dynamic change characteristics, it is always the focus of network topology measurement to obtain as many network paths as possible in a short time. In this paper, we propose a large-scale network path probing approach in order to solve the problems of low probing efficiency and high probing redundancy commonly found in current research. By improving the packet delivery order and the update strategy of time-to-live field values, we redesigned and implemented an efficient large-scale network path probing tool. The experimental results show that the method-derived tool can complete path probing for a sample of 12 million/24 network address segments worldwide within 1 hour, which greatly improves the efficiency of network path probing. Meanwhile, compared to existing methods, the proposed method can reduce the number of packets sent by about 10% with the same number of network addresses found, which effectively reduces probing redundancy and alleviates the network load.