Research on the derivation of AS hidden links and the Discovery of Critical AS

AS relationships are the basis for studying Internet security, route hijacking, route leakage, etc. To obtain more complete AS relationships, using machine learning (ML) models to learn the similarity between adjacent link groups and predict hidden links is a method that can obtain more complete AS relationships. The features selected by the ML model have a large impact on the accuracy of the prediction results, and we extract 10 ML features by combining the actual geographic location information of AS. After our optimization, the accuracy of the prediction model reaches 91.57%. In the classification of hidden link types, we oversample the small sample type data and optimize the classifier, and the classification accuracy of hidden link categories reaches 97.42%. The recall rate of p2c and c2p links improved by 24.29% and 7.17%, respectively. We found that the hidden links caused the change of network traffic transmission routes by the change of "Critical AS" in the AS network. AS 3549 has the highest number of effective paths, and the network traffic prefers to choose the AS with a lower hierarchy for forwarding.