Using RPKI to Aggregate Autonomous Systems by their Managing Organization

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.