Cutting Through the Noise to Infer Autonomous System Topology
Abstract
The Border Gateway Protocol (BGP) is a distributed protocol that manages interdomain routing without requiring a centralized record of which autonomous systems (ASes) connect to which others. Many methods have been devised to infer the AS topology from publicly available BGP data, but none provide a general way to handle the fact that the data are notoriously incomplete and subject to error. This paper describes a method for reliably inferring AS-level connectivity in the presence of measurement error using Bayesian statistical inference acting on BGP routing tables from multiple vantage points. We employ a novel approach for counting AS adjacency observations in the AS-PATH attribute data from public route collectors, along with a Bayesian algorithm to generate a statistical estimate of the AS-level network. Our approach also gives us a way to evaluate the accuracy of existing reconstruction methods and to identify advantageous locations for new route collectors or vantage points.
Keywords
Cite
@article{arxiv.2201.07328,
title = {Cutting Through the Noise to Infer Autonomous System Topology},
author = {Kirtus G. Leyba and Joshua J. Daymude and Jean-Gabriel Young and M. E. J. Newman and Jennifer Rexford and Stephanie Forrest},
journal= {arXiv preprint arXiv:2201.07328},
year = {2022}
}
Comments
10 pages, 8 figures, 1 table. To appear at IEEE INFOCOM 2022. \copyright\ IEEE 2022