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Revealing the Autonomous System Taxonomy: The Machine Learning Approach
Xenofontas Dimitropoulos
Georgia Tech and Cooperative Assocation for Internet Data Analysis - CAIDA
Dmitri Krioukov
Cooperative Association for Internet Data Analysis - CAIDA
San Diego Supercomputer Center,
University of California, San Diego
George Riley
Georgia Tech
kc claffy
Cooperative Association for Internet Data Analysis - CAIDA
San Diego Supercomputer Center,
University of California, San Diego
Although the Internet AS-level topology has been extensively studied over the past few years, little is known about the details of the AS taxonomy. An AS "node" can represent a wide variety of organizations, e.g., large ISP, or small private business, university, with vastly different network characteristics, external connectivity patterns, network growth tendencies, and other properties that we can hardly neglect while working on veracious Internet representations in simulation environments. In this paper, we introduce a radically new approach based on machine learning techniques to map all the ASes in the Internet into a natural AS taxonomy. We successfully classify 95.3% of ASes with expected accuracy of 78.1%. We release to the community the AS-level topology dataset augmented with: 1) the AS taxonomy information and 2) the set of AS attributes we used to classify ASes. We believe that this dataset will serve as an invaluable addition to further understanding of the structure and evolution of the Internet.