A Novel Methodologyof Router-To-ASMapping inspired by Community Discovery

TL;DR

This paper introduces a community discovery-based methodology for router-to-AS mapping that significantly improves accuracy by leveraging topology and community structures, outperforming previous methods.

Contribution

The paper presents a novel community discovery approach for router-to-AS mapping, utilizing large-scale topology data and hierarchical clustering for enhanced accuracy.

Findings

Achieves 82.62% accuracy in router-to-AS mapping

Uses linear time and space complexity clustering for large datasets

Outperforms prior methods with 65.44% accuracy

Abstract

In the last decade many works has been done on the Internet topology at router or autonomous system (AS) level. As routers is the essential composition of ASes while ASes dominate the behavior of their routers. It is no doubt that identifying the affiliation between routers and ASes can let us gain a deeper understanding on the topology. However, the existing methods that assign a router to an AS just based on the origin ASes of its IP addresses, which does not make full use of information in our hand. In this paper, we propose a methodology to assign routers to their owner ASes based on community discovery tech. First, we use the origin ASes information along with router-pairs similarities to construct a weighted router level topology, secondly, for enormous topology data (more than 2M nodes and 19M edges) from CAIDA ITDK project, we propose a fast hierarchy clustering which time and space complex are both linear to do ASes community discovery, last we do router-to-AS mapping based on these ASes communities. Experiments show that combining with ASes communities our methodology discovers, the best accuracy rate of router-to-AS mapping can reach to 82.62%, which is drastically high comparing to prior works that stagnate on 65.44%.