Unveiling the Type of Relationship between Autonomous Systems Using Deep Learning

Tal Shapira, Yuval Shavitt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The ToR inference problem had been widely investigated in the last two decades, mostly using heuristic algorithms. In this problem, we attempt to reveal the economic relationships between ASes, data with applications in network routing management and routing security.In this paper, we introduce a novel approach for ToR classification, which is based on embedding the AS numbers (ASN) in high dimensional space using neural networks. Similar to natural language processing (NLP) models, the embedding represents latent characteristics of the ASN and its interactions on the Internet. The embedding coordinates of each AS are represented by a vector; thus, we call our method BGP2VEC. In order to solve the supervised learning problem presented, we use these vectors as an input to an artificial neural network and achieve a state of the art accuracy of 95.2% for ToR classification.

Original languageEnglish
Title of host publicationProceedings of IEEE/IFIP Network Operations and Management Symposium 2020
Subtitle of host publicationManagement in the Age of Softwarization and Artificial Intelligence, NOMS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149738
DOIs
StatePublished - Apr 2020
Event2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020 - Budapest, Hungary
Duration: 20 Apr 202024 Apr 2020

Publication series

NameProceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020

Conference

Conference2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Country/TerritoryHungary
CityBudapest
Period20/04/2024/04/20

Keywords

  • AS embedding
  • AS relationships
  • BGP
  • Deep Learning
  • Internet

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