## 4 Experimental Results and Discussion
### Network Centrality Metrics  
This section presents the results of the network centrality measure analysis for the four threat vectors (\(tv_{13}\), \(tv_{23}\), \(tv_{123\text{-}MEO}\), and \(tv_{123\text{-}LEO}\)). The analysis used the degree centrality and betweenness centrality, and we evaluated the network characteristics of each scenario while using five patch strategies: no patch, OS patch, protocol patch, both patches, and CFR. Table 6 presents the degree centrality analysis results. The experimental evaluation quantitatively assessed the impacts of network nodes and each patch strategy on the degree centrality. This analysis quantitatively explains how security patch application influences the network structure. In scenario \(tv_{13}\), the _RT_ emerged as the node with the highest average degree centrality, consistently serving as a key hub in the network. Both the OS patch and protocol patch reduced the centralities of the _FW_ and _RT_, and when applying both patches and CFR, the centrality of the _RT_ further decreased, contributing to a reduction in the attack surface. This indicates that the _RT_ acts as a critical traffic hub in the network, and specific patch strategies help to distribute the load more effectively. In \(tv_{23}\), the _IoT_ and \(GW_{2}\) recorded the highest centrality values, playing crucial roles as central connecting nodes within the user segment. Notably, the protocol patch and both patches reduced the centrality of \(GW_{1}\), making the network structure more balanced and effectively reducing exposure to potential attacks. In \(tv_{123\text{-}MEO}\), _MEO_ satellites, the _RT_, \(GW_{1}\), and \(GW_{2}\) maintained similar centrality values, indicating balanced network importance between the space segment and ground segment. When applying both patches, the centrality values for all the nodes tended to decrease, which suggests that the patching strategy helps to reduce the attack surface while maintaining the  
Figure 8: Attack impact metrics for security assessment. network connectivity. In \(tv_{123\_LEO}\), \(GW_{1}\) exhibited the highest centrality, followed by \(LEO\) satellites and \(GW_{2}\). Both patches and CFR significantly reduced the centralities of \(GW_{1}\) and \(GW_{2}\), reducing exposure along the key network paths and strengthening the overall security of the network. Next, as shown in Table 7, we analyzed the impacts of the network nodes and patch strategies on the betweenness centrality, which indicates how often a node serves as a bridge in data transmission paths. Higher betweenness centrality values indicate that a node is important in routing traffic within the network. This analysis allows us to quantitatively assess the effects of specific patch strategies on the traffic distribution and attack path blocking. In \(tv_{13}\), the \(RT\) and the \(GCS\) showed the highest betweenness centralities, acting as the key intermediaries within the network. The application of the OS patch and protocol patch decreased the \(RT\)'s betweenness centrality, facilitating a more even network traffic distribution. In contrast, applying both patches and CFR further reduced the \(RT\)'s centrality, leading to more effective balancing of the traffic load and reducing the attack surface. In \(tv_{23}\), the \(IoT\) and \(GW_{2}\) exhibited the highest betweenness centralities, playing a crucial intermediary role within the user segment. When applying both patches, the betweenness centralities for \(GW_{1}\) and \(GW_{2}\) notably decreased, mitigating the concentration of the traffic on specific nodes and distributing attack paths more effectively. In \(tv_{123\_MEO}\), the \(RT\) maintained the highest betweenness centrality, serving as a critical intermediary in the network's main data paths. The application of both patches led to reductions in the betweenness centralities of \(GW_{1}\) and \(GW_{2}\), effectively isolating the key nodes and decreasing the likelihood of attacks targeting specific nodes.