ACM’s Internet Measurement Conference (IMC) is an annual highly selective venue for the presentation of Internet measurement and analysis research. The average acceptance rate for papers is around 25%. CAIDA researchers co-authored three papers and one poster that was be presented at the IMC conference in Montreal, Quebec on October 26 – 28, 2023. We link to these publications below.

On the Importance of Being an AS: An Approach to Country-Level AS Rankings
Bradley Huffaker, Alexander Marder, Romain Fontugne, kc claffy. ACM Internet Measurement Conference (IMC), 2023.
Recent geopolitical events demonstrate that control of Internet infrastructure in a region is critical to economic activity and defense against armed conflict. This geopolitical importance necessitates novel empirical techniques to assess which countries remain susceptible to degraded or severed Internet connectivity because they rely heavily on networks based in other nation states. Currently, two preeminent BGP-based methods exist to identify influential or market-dominant networks on a global scale-network-level customer cone size and path hegemony–but these metrics fail to capture regional or national differences.

We adapt the two global metrics to capture country-specific differences by restricting the input data for a country-specific metric to destination prefixes in that country. Although conceptually simple, our study required tackling methodological challenges common to most Internet measurement research today, such as geolocation, incomplete data, vantage point access, and lack of ground truth. Restricting public routing data to individual countries requires substantial downsampling compared to global analysis, and we analyze the impact of downsampling on the robustness and stability of our country-specific metrics. As a measure of validation, we apply our country-specific metrics to case studies of Australia, Japan, Russia, Taiwan, and the United States, illuminating aspects of concentration and interdependence in telecommunications markets. To support reproducibility, we will share our code, inferences, and data sets with other researchers.

IRRegularities in the Internet Routing Registry
Ben Du, Gautam Akiwate, Cecilia Testart, Alex C. Snoeren, kc claffy, Katherine Izhikevich, Sumanth Rao. ACM Internet Measurement Conference (IMC), 2023.
The Internet Routing Registry (IRR) is a set of distributed databases used by networks to register routing policy information and to validate messages received in the Border Gateway Protocol (BGP). First deployed in the 1990s, the IRR remains the most widely used database for routing security purposes, despite the existence of more recent and more secure alternatives. Yet, the IRR lacks a strict validation standard and the limited coordination across diferent database providers can lead to inaccuracies. Moreover, it has been reported that attackers have begun to register false records in the IRR to bypass operators’ defenses when launching attacks on the Internet routing system, such as BGP hijacks. In this paper, we provide a longitudinal analysis of the IRR over the span of 1.5 years. We develop a workflow to identify irregular IRR records that contain conflicting information compared to different routing data sources. We identify 34,199 irregular route objects out of 1,542,724 route objects from November 2021 to May 2023 in the largest IRR database and find 6,373 to be potentially suspicious.

Coarse-grained Inference of BGP Community Intent
Thomas Krenc, Alexander Marder, Matthew Luckie, kc claffy. ACM Internet Measurement Conference (IMC), 2023.
BGP communities allow operators to influence routing decisions made by other networks (action communities) and to annotate their network’s routing information with metadata such as where each route was learned or the relationship the network has with their neighbor (information communities). BGP communities also help researchers understand complex Internet routing behaviors. However, there is no standard convention for how operators assign community values, and significant efforts to scalably infer community meanings have ignored this high-level classification. We discovered that doing so comes at a significant cost in accuracy, of both inference and validation. To advance this narrow but powerful direction in Internet infrastructure research, we design and validate an algorithm to execute this first fundamental step: inferring whether a BGP community is action or information. We applied our method to 78,480 community values observed in public BGP data for May 2023. Validating our inferences (24,376 action and 54,104 informational communities) against available ground truth (6,259 communities) we find that our method classified 96.5% correctly. We found that the precision of a state-of-the-art location community inference method increased from 68.2% to 94.8% with our classifications. We publicly share our code, dictionaries, inferences, and datasets to enable the community to benefit from them.

CAIDA also contributed to one extended abstract:

Empirically Testing the PacketLab Model
Tzu-Bin Yan, Zesen Zhang, Bradley Huffaker, Ricky K. P. Mok, kc claffy, Kirill Levchenko. ACM Internet Measurement Conference (IMC) Poster, 2023.
PacketLab is a recently proposed model for accessing remote vantage points. The core design is for the vantage points to export low-level network operations that measurement researchers could rely on to construct more complex measurements. Motivating the model is the assumption that such an approach can overcome persistent challenges such as the operational cost and security concerns of vantage point sharing that researchers face in launching distributed active Internet measurement experiments. However, the limitations imposed by the core design merit a deeper analysis of the applicability of such model to real-world measurements of interest. We undertook this analysis based on a survey of recent Internet measurement studies, followed by an empirical comparison of PacketLab-based versus native implementations of common measurement methods. We showed that for several canonical measurement types common in past studies, PacketLab yielded similar results to native versions of the same measurements. Our results suggest that PacketLab could help reproduce or extend around 16.4% (28 out of 171) of all surveyed studies and accommodate a variety of measurements from latency, throughput, network path, to non-timing data.