Abstract
The last decades have witnessed the progressive development of research on Internet topology at the router or autonomous systems (AS) level. Routers are essential components of ASes, which dominate their behaviors. It is important to identify the affiliation between routers and ASes because this contributes to a deeper understanding of the topology. However, the existing methods that assign a router to an AS, based on the origin AS of its IP addresses do not make full use of the information during the network interaction procedure. In this paper, we propose a novel method to assign routers to their owners’ AS, based on community discovery. First, we use the initial AS information along with router-pair similarities to construct a weighted router level graph; secondly, with the large amount of graph data (more than 2M nodes and 19M edges) from the CAIDA ITDK project, we propose a fast hierarchy clustering algorithm with time and space complexity, which are both linear for graph community discovery. Finally, router-to-AS mapping is completed, based on these AS communities. Experimental results show that the effectiveness and robustness of the proposed method. Combining with AS communities, our method could have the higher accuracy rate reaching to 82.62% for Routers-to-AS mapping, while the best accuracy of prior works is plateaued at 65.44%.
Highlights
As a hot topic in the network science research area, the topology of the Internet has drawn considerable attention from many researchers
We propose a fast hierarchy clustering with time and space complexity which are both linear, We propose a fast with time and space complexity which are both linear, which is capable of hierarchy finding ASclustering communities; which is capable our of finding
There are only 617 routers that satisfy the condition; Step 2: We looked up the router-level topology generated before to examine whether these routers were isolated nodes or not, and we find that there are 3 of them which are solo nodes, and we take them out; Step 3: We took out the current router’s port and IP address, which have unknown autonomous systems (AS) information; for example, if router-A has 3 IP hosts where AS refers to AS1, AS2, and AS3 according to the IP
Summary
As a hot topic in the network science research area, the topology of the Internet has drawn considerable attention from many researchers. Enrico Gregori et al [13] analyzed BGP data by the router collector project and found that large areas of the Internet are not properly captured, due to the geographical location of route collector feeders and BGP filters They proposed a method based on a new metric, named p2c-distance, to identify the minimum number of AS required to obtain an Internet AS-level topology. Huffaker et al designed five router assignment heuristics based on the origin AS of routers, and validated them on the ground truth provided by two ISPs and five research networks They tested all combinations of pairs of heuristics, and found that the most successful pair was election + degree. We demonstrate our method using community discovery upon a weighted router-level graph, can lead to a drastic increase of the accuracy rate.
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