The Domain name system (DNS) is crucial to Internet services. Previous studies have pointed out that the name dependency in domain name resolution poses a risk to the security of DNS. In this paper, we present measurement results from a dataset containing resolution paths of domain names collected from a large-scale survey. This dataset is used to research the effect of the name dependency on the DNS, reaffirm findings in published work, and notice some significant differences. When name resolution spans multiple domains, it will lead to name dependency and make the resolution process more complex. Furthermore, we assess the risk of domain name resolution: a name resolution fault analysis model and the calculation of the failure probability of name resolution is proposed. The model can identify the key server sets that lead to the resolution failure of a domain name, and quantify the failure probability of its resolution. This research provides a breakthrough point for guiding the configuration, management, deployment, and upgrading of the DNS.

In the process of resolving domain names to IP addresses, there exist complex dependence relationships between domains and name servers. This paper studies the impact of the resolution dependence on the DNS through constructing a domain name resolution network based on large-scale actual data. The core nodes of the resolution network are mined from different perspectives by means of four methods. Then, both core attacks and random attacks on the network are simulated for further vulnerability analysis. The experimental results show that when the top 1% of the core nodes in the network are attacked, 46.19% of the domain names become unresolved, and the load of the residual network increases by nearly 195%, while only 0.01% of domain names fail to be resolved and the load increases with 18% in the same attack scale of the random mode. For these key nodes, we need to take effective security measures to prevent them from being attacked. The simulation experiment also proves that the resolution network is a scale-free network, which exhibits robustness against random failure and vulnerability against intentional attacks. These findings provide new references for the configuration of the DNS.

Due to the limitations of physical resources, if a large-scale emulation network environment composed of millions of vertices and edges is constructed by virtualization technology, the whole network topology should be partitioned into a set of subnets. The topology partition is a work of graph partition. The existing topology partition methods have shortcomings, such as low efficiency and poor practicability, especially for large-scale network topology. The emulation network is a kind of complex network and has the characteristics of community structure. Therefore, we proposed LENTP (large-scale emulation network topology partition) based on the community detection with the weight of the vertex similarity for large-scale topology partition. In the first stage, the tree-structured area compression reduces the topology scales significantly to improve partition efficiency. And then, the improved Louvain algorithm is used to topology partitioning and obtain an initial set of subnets with the minimum number of subnets and remote links. Finally, after repartitioning and merging for the initial subnets, the result of subnets is the final topology partition that reaches the optimization objectives with the conditions of the virtual resources. In the experiment, the method is tested in five groups of network topology with different scales. The results demonstrate that LENTP can partition the network topology over 1 000 000 nodes and significantly improve the running-time efficiency of the network topology partition.