Space-ground integrated network, a strategic, driving, and irreplaceable infrastructure, guarantees the development of economic and national security. However, its natures of limited resources, frequent handovers, and intermittently connected links significantly reduce the quality of service. To address this issue, a quality-of-service-aware dynamic evolution model is proposed based on complex network theory. On one hand, a quality-of-service-aware strategy is adopted in the model. During evolution phases of growth and handovers, links are established or deleted according to the qualityof-service-aware preferential attachment following the rule of better quality of service getting richer and worse quality of service getting poor or to die. On the other hand, dynamic handover of nodes and intermittent connection of links are taken into account and introduced into the model. Meanwhile, node heterogeneity is analyzed and heterogeneous nodes are endowed with discriminate interactions. Theoretical analysis and simulations are utilized to explore the degree distribution and its characteristics. Results reveal that this model is a scale-free model with drift power-law distribution, fat-tail and small-world effect, and drift character of degree distribution results from dynamic handover. Furthermore, this model exerts well fault tolerance and attack resistance compared to signal-strength-based strategy. In addition, node heterogeneity and quality-of-service-aware strategy improve the attack resistance and overall quality of service of space-ground integrated network.
Reconfigurable security protocols, with dynamic protocol configuration and flexible resource allocation, have become a state-of-the-art technology to guarantee the security of space-ground integrated network. However, reconfiguration decision-making for reconfigurable security protocols remains a major challenge in order to adapt to diverse secure service requirements and deploy higher security level but more complicated security strategies in nodes with limited resources and computing abilities. To handle this problem commendably, a hierarchically collaborative ant colony–based reconfiguration decision-making model called HiCoACR is proposed. This model, inspired by the ideas of hierarchical reinforcement learning and population collaboration, decomposes the reconfiguration decision-making problem into two sub-problems by introducing a two-level hierarchy ant colony consisting of the Explorer and the Worker. The Explorer controls directions of protocol reconfiguration and generates abstract scheduling sub-goals which are conveyed from the Worker. While the Worker schedules most suitable cryptogram resources for each sub-goal received and produces the optimal reconfiguration solution which is verified and re-optimized by a Lévy process–based stochastic gradient descent algorithm. Both the Explorer and the Worker adopt a modified version of ant colony algorithm to fulfill its targets, where a hierarchical pheromone is defined to reinforce positive behaviors of each ant colony. Experiment results suggest that HiCoACR outperforms baseline algorithms and possesses well model transferability.
Assembly of reconfigurable security protocol remains a major challenge for deploying higher security-level but more complicated security strategies in access points with limited resources and computing abilities. To handle this problem commendably, a hierarchically collaborative ant colony-based assembly algorithm was proposed. This algorithm decomposed the security protocol assembly problem into assembling directions controlling sub-task and cryptographic components selection sub-task. Directions control generated assembly subgoals and cryptographic components selection schedules the bestfitted components for given sub-goals. Both sub-tasks adopted a modified version of ant colony algorithm to fulfil its targets. These two ant colony algorithms generate a candidate optimal solution collaboratively for the assembly problem. And a hierarchical pheromone was defined to reinforce positive behaviors of ant colony. Additionally, a Lévy theory based stochastic gradient algorithm was adopted to verify and reoptimize the optimal solution. Experiment results suggest that the proposed algorithm outperforms baseline algorithms in convergence and performance.
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