metAScritic: Reframing AS-Level Topology Discovery as a Recommendation System - LAAS-Réseaux et Communications
Conference Papers Year : 2024

metAScritic: Reframing AS-Level Topology Discovery as a Recommendation System

Abstract

Despite prior efforts, the vast majority of the AS-level topology of the Internet remains hidden from BGP and traceroute vantage points. In this work, we introduce metAScritic, a novel system inspired by recommender system literature, designed to infer interconnections within a given metro. metAScritic uses the intuition that the connectivity matrix at a given metro is a low-rank system, since ASes employ similar peering strategies according to their infrastructures, traffic profiles, and business models. This approach allows metAScritic to accurately reconstruct the complete peering connectivity by measuring a strategic subset of interconnections that capture ASes' underlying peering strategies. We evaluate metAScritic's performance across six large metropolitan areas, achieving an average F-score of 0.88 on various validation datasets, including ground truth. metAScritic measures more than 86K edges and infers more than 368K edges, compared to the 13K edges observed for this subset of ASes in public BGP feeds -an increase of 24× what is currently seen. We study the impact of our inferred links on Internet properties, illustrating the extent of the Internet's flattening and demonstrating our ability to better predict the impact of route leaks and prefix hijacks, compared to relying only on the existing public view.
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Dates and versions

hal-04719057 , version 1 (07-10-2024)

Identifiers

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Loqman Salamatian, Kevin Vermeulen, Italo Cunha, Vasilis Giotsas, Ethan Katz-Bassett. metAScritic: Reframing AS-Level Topology Discovery as a Recommendation System. ACM Internet Measurement Conference (IMC ’24), ACM, Nov 2024, Madrid, Spain. ⟨10.1145/3646547.3688429⟩. ⟨hal-04719057⟩
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