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Accurate mapping of Autonomous Systems (ASes) to their owner organizations is fundamental for understanding the structure and dynamics of the Internet. However, as AS numbers have traditionally been delegated in an ad-hoc manner and organizational ownership has evolved over time, many organizations have registered resources under different names. Traditionally, researchers have relied on datasets like AS2Org, which map ASNs to organizations primarily using WHOIS records, but WHOIS inconsistencies often lead to missed and false relationships. We propose a new approach by leveraging the Resource Public Key Infrastructure (RPKI) to map ASNs to their managing organization. Our methodology combines multiple data sources: WHOIS records to extract organization names, RPKI certificates to identify potential siblings, and Large Language Models (LLMs) to find evidence not visible in WHOIS records currently. This integrated approach enables a more robust and accurate mapping of ASNs to organizations, notably improving inferences for 14% of multi-ASN clusters.
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Learning to Extract and Use ASNs in Hostnames
We present the design, implementation, evaluation, and validation of a system that learns regular expressions (regexes) to extract Autonomous System Numbers (ASNs) from hostnames associated with router interfaces. We train our system with ASNs inferred by RouterToAsAssignment and bdrmapIT using topological constraints from traceroute paths, as well as ASNs recorded by operators in PeeringDB, to learn regexes for 206 different suffixes. Because these methods for inferring router ownership can infer the wrong ASN, we modify bdrmapIT to integrate this new capability to extract ASNs from hostnames. Evaluating against ground truth, our modification correctly distinguished stale from correct hostnames for 92.5% of hostnames with an ASN different from bdrmapIT’s initial inference. This modification allowed bdrmapIT to increase the agreement between extracted and inferred ASNs for these routers in the January 2020 ITDK from 87.4% to 97.1% and reduce the error rate from 1/7.9 to 1/34.5. This work presents a new avenue for collecting validation data, opening a broader horizon of opportunity for evidence-based router ownership inference.
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- PAR ID:
- 10289016
- Date Published:
- Journal Name:
- IMC '20: Proceedings of the ACM Internet Measurement Conference
- Page Range / eLocation ID:
- 386 to 392
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3419394.3423639
