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| Title | Poster: Application of Graph Neural Networks for Representing and Analyzing the Internet Topology via the BGP Protocol |
| Authors | Valentina Esteban, Ivana Bachmann, Sebastian Ferrada |
| Publication date | 2024 |
| Abstract | The relationships between Autonomous Systems (ASes) is a crucial aspect of the Internet, as they reveals how it operates and influence in the routing decision, as well as identifying BGP anomalies. However, most of the time this information is confidential, given that each AS is independently manage by different entities. This work aims to infer the types of relationships between ASes using Graph Neural Network (GNN). The Type of Relationship (ToR) problem has been a topic of studied for the past two decades, with most solutions being heuristic. One of the biggest challenges this problem presents is the lack of ground truth information to validate the results. Our preliminary results show an accuracy of 0.943 for binary classification and 0.936 for multiclass classification. |
| Downloaded | 18 times |
| Pages | 787-788 |
| Conference name | ACM Internet Measurement Conference |
| Publisher | ACM Press (New York, NY, USA) |
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