Unveiling the Type of Relationship Between Autonomous Systems Using Deep Learning
Published in NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, 2020
Recommended citation: T. Shapira and Y. Shavitt, "Unveiling the Type of Relationship Between Autonomous Systems Using Deep Learning," NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 2020, pp. 1-6. https://ieeexplore.ieee.org/document/9110358
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.