BGP2Vec: Unveiling the Latent Characteristics of Autonomous Systems
- Resource Type
- Periodical
- Authors
- Shapira, T.; Shavitt, Y.
- Source
- IEEE Transactions on Network and Service Management IEEE Trans. Netw. Serv. Manage. Network and Service Management, IEEE Transactions on. 19(4):4516-4530 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Inference algorithms
Routing
Internet
Heuristic algorithms
IP networks
Deep learning
Approximation algorithms
BGP
routing
ASN embedding
AS classification
AS relationships
deep learning
- Language
- ISSN
- 1932-4537
2373-7379
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.