透过噪声推断自治系统拓扑
Cutting Through the Noise to Infer Autonomous System Topology
AI总结：
提出一种基于贝叶斯统计推断的方法，从多视角BGP路由表中可靠地推断AS级连接，并评估现有重建方法的准确性。
摘要：
边界网关协议（BGP）是一种分布式协议，管理域间路由，无需集中记录哪些自治系统（AS）相互连接。已经设计了许多方法从公开的BGP数据推断AS拓扑，但没有一种方法提供通用的方式来处理数据众所周知的不完整和容易出错的事实。本文描述了一种在存在测量误差的情况下，使用贝叶斯统计推断作用于多视角BGP路由表来可靠推断AS级连接的方法。我们采用了一种新颖的方法来统计来自公共路由收集器的AS-PATH属性数据中的AS邻接观察，并结合贝叶斯算法生成AS级网络的统计估计。我们的方法还使我们能够评估现有重建方法的准确性，并确定新路由收集器或视角的有利位置。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
