Using RPKI to Aggregate Autonomous Systems by their Managing Organization
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".