Improving the Inference of Sibling Autonomous Systems.

Correctly mapping Autonomous Systems (ASes) to their owner organizations is critical for connecting AS-level and organization-level research. Unfortunately, constructing an accurate dataset of AS-to-organization mappings is difficult due to a lack of ground truth information. CAIDA AS-to-organization (CA2O), the current state-of-the-art dataset, relies heavily on Whois databases maintained by Regional Internet Registries (RIRs) to infer the AS-to-organization mappings. However, inaccuracies in Whois data can dramatically impact the accuracy of CA2O, particularly for inferences involving ASes owned by the same organization (referred to as sibling ASes). In this work, we leverage PeeringDB (PDB) as an additional data source to detect potential errors of sibling relations in CA2O. By conducting a meticulous semi-manual investigation, we discover two pitfalls of using Whois data that result in incorrect inferences in CA2O. We then systematically analyze how these pitfalls influence CA2O. We also build an improved dataset on sibling relations, which corrects the mappings of 12.5% of CA2O organizations with sibling ASes (1,028 CA2O organizations, associated with 3,772 ASNs). To make this process reproducible and scalable, we design an automated approach to recreate our manually-built dataset with high fidelity. The approach is able to automatically improve inferences of sibling ASes for each new version of CA2O.