Abstract-Despite significant efforts to obtain an accurate picture of the Internet's connectivity structure at the level of individual autonomous systems (ASes), much has remained unknown in terms of the quality of the inferred AS maps that have been widely used by the research community. In this paper we assess the quality of the inferred Internet maps through case studies of a sample set of ASes. These case studies allow us to establish the ground truth of connectivity between this set of ASes and their directly connected neighbors. A direct comparison between the ground truth and inferred topology maps yield insights into questions such as which parts of the actual topology are adequately captured by the inferred maps, which parts are missing and why, and what is the percentage of missing links in these parts. This information is critical in assessing, for each class of real-world networking problems, whether the use of currently inferred AS maps or proposed AS topology models is, or is not, appropriate. More importantly, our newly gained insights also point to new directions towards building realistic and economically viable Internet topology maps.
A prefix hijack attack involves an attacker announcing victim networks' IP prefixes into the global routing system. As a result, data traffic from portions of the Internet can be diverted to attacker networks. Prefix hijack attacks are a serious security threat in the Internet and it is important to understand the factors that affect the resiliency of victim networks against these attacks. In this paper, we conducted a systematic study to gauge the effectiveness of prefix hijacks launched at different locations in the Internet topology. Our study shows that direct customers of multiple tier-1 networks are the most resilient, even more than the tier-1 networks themselves. Conversely, if these customer networks are used to launch prefix hijacks, they would also be the most effective launching pads for attacks. We verified our results through case studies using real prefix hijack incidents that had occurred in the Internet.
Characterizing the evolution of Internet topology is important to our understanding of the Internet architecture and its interplay with technical, economic and social forces. A major challenge in obtaining empirical data on topology evolution is to identify real topology changes from the observed topology changes, since the latter can be due to either topology changes or transient routing dynamics. In this paper, we formulate the topology liveness problem and propose a solution based on the analysis of BGP data. We find that the impact of transient routing dynamics on topology observation decreases exponentially over time, and that the real topology dynamics consist of a constant-rate birth process and a constant-rate death process. Our model enables us to infer real topology changes from observation data with a given confidence level. We demonstrate the usefulness of the model by applying it to three applications: providing more accurate views of the topology, evaluating theoretical evolution models, and empirically characterizing the trends of topology evolution. We find that customer networks and provider networks have distinct evolution trends, which can provide an important input to the design of future Internet routing architecture.
Characterizing the evolution of Internet topology is important to our understanding of the Internet architecture and its interplay with technical, economic and social forces. A major challenge in obtaining empirical data on topology evolution is to identify real topology changes from the observed topology changes, since the latter can be due to either topology changes or transient routing dynamics. In this paper, we formulate the topology liveness problem and propose a solution based on the analysis of BGP data. We find that the impact of transient routing dynamics on topology observation decreases exponentially over time, and that the real topology dynamics consist of a constant-rate birth process and a constant-rate death process. Our model enables us to infer real topology changes from observation data with a given confidence level. We demonstrate the usefulness of the model by applying it to three applications: providing more accurate views of the topology, evaluating theoretical evolution models, and empirically characterizing the trends of topology evolution. We find that customer networks and provider networks have distinct evolution trends, which can provide an important input to the design of future Internet routing architecture.
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