论文标题
切开噪声以推断自主系统拓扑
Cutting Through the Noise to Infer Autonomous System Topology
论文作者
论文摘要
边界网关协议（BGP）是一个分布式协议，该协议可以管理域间路由，而无需在哪些自主系统（ASES）连接到其他的集中记录。已经设计了许多方法来从公开可用的BGP数据中推断出AS拓扑，但是没有一个提供一种通用的方法来处理众所周知数据不完整且遭受错误的事实。本文介绍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从多个Vantage点上对BGP路由表上的贝叶斯统计推断的方法可靠地推断出AS级连接的方法。我们采用一种新颖的方法来计算来自公共路线收集器的AS-PATH属性数据中的邻接观测，以及贝叶斯算法，以生成对AS级网络的统计估计。我们的方法还为我们提供了一种评估现有重建方法准确性并确定新路线收集器或有利位置的优势位置的方法。
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.
