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Poster: A Dataset on Peering Policies of Autonomous Systems

Published: 04 November 2024 Publication History

Abstract

This paper presents a methodology for extracting and structuring information from Internet Autonomous Systems peering policy documents using natural language processing techniques. We trained a named entity recognition model to identify and extract key entities related to peering practices. The resulting structured dataset, made publicly available, provides valuable insights into autonomous system peering requirements, preferences, and routing practices. This dataset serves as a foundation for understanding and modelling the peer selection processes of autonomous systems on the Internet. Our ongoing work focuses on developing a policy-aware approach to select peering partners based on compatibility scores derived from the extracted policy requirements.

References

[1]
CAIDA. 2024. PeeringDB Archive. https://www.caida.org/catalog/datasets/peeringdb/. (Accessed on 05/13/2024).
[2]
FyfeWeb. 2024. FyfeWeb Peering Policy. https://fyfeweb.com/downloads/legal/peering-policy.pdf. (Accessed on 07/10/2024).
[3]
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020. spaCy: Industrial-strength Natural Language Processing in Python. (2020). https://doi.org/10.5281/zenodo.1212303
[4]
Microsoft. 2024. Fast and reliable end-to-end testing for modern web apps | Playwright Python. https://playwright.dev/python/. (Accessed on 07/10/2024).
[5]
William Norton. 2014. The Internet Peering Playbook: Connecting to the Core of the Internet 2014 edition ed.). DrPeering Press.
[6]
PeeringDB. 2024. PeeringDB. https://peeringdb.com/. (Accessed on 05/13/2024).
[7]
SpaCy. 2024. Training Pipelines & Models. https://spacy.io/usage/training. (Accessed on 07/10/2024).

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Published In

cover image ACM Conferences
IMC '24: Proceedings of the 2024 ACM on Internet Measurement Conference
November 2024
812 pages
ISBN:9798400705922
DOI:10.1145/3646547
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2024

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Author Tags

  1. autonomous systems
  2. information extraction
  3. internet peering
  4. peering policies

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IMC '24
IMC '24: ACM Internet Measurement Conference
November 4 - 6, 2024
Madrid, Spain

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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