BGP2Vec: Unveiling the Latent Characteristics of Autonomous Systems

Tal Shapira, Yuval Shavitt

Research output: Contribution to journalArticlepeer-review

Abstract

BGP announcements hold latent information about the Internet Autonomous Systems (ASes) and their functional position within the Internet eco-system. This information can aid us in understanding the Internet structure and also in solving many practical problems. In this paper, we present BGP2Vec, a novel approach to revealing the latent characteristics of ASes using neural-network-based embedding. We show that our embedding indeed captures important characteristics of ASes, and then show how the embedding can be used to solve two problems: ASN business-type classification and AS Type of Relationships (ToRs) inference. ToRs inference has been heavily studied in the past two decades and is important for studying internet routing and identifying IP hijack attacks. We use the BGP2Vec vectors as an input to artificial neural networks and achieve excellent results: an accuracy of 95.8% for ToR classification and an accuracy of 79.2% for AS classification.

Original languageEnglish
JournalIEEE Transactions on Network and Service Management
DOIs
StateAccepted/In press - 2022

Keywords

  • AS classification
  • AS relationships
  • ASN embedding
  • Approximation algorithms
  • BGP
  • Deep Learning.
  • Deep learning
  • Heuristic algorithms
  • IP networks
  • Inference algorithms
  • Internet
  • Internet
  • Routing
  • routing

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