AuthorsA. Lutu, J. Cid-Sueiro and O. Maennel
TitleSeparating wheat from chaff: Winnowing unintended prefixes using machine learning
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2014
Conference NameINFOCOM, 2014 Proceedings IEEE
Pagination943–951
PublisherIEEE
ISSN Number0743-166X
Abstract

In this paper, we propose the use of prefix visibility at the interdomain level as an early symptom of anomalous events in the Internet. We focus on detecting anomalies which, despite their significant impact on the routing system, remain concealed from state of the art tools. We design a machine learning system to winnow the prefixes with unintended limited visibility - symptomatic of anomalous events - from the prefixes with intended limited visibility - resulting from legitimate routing operations. We train a winnowing algorithm with ground-truth data on 20,000 operational limited visibility prefixes (LVPs) already classified by the operators of the origin networks. The ground-truth was collected using the BGP Visibility Scanner, a tool we developed to provide operators with a multi-angle view on the efficacy of their routing policies. We build a dataset with the pre-classified prefixes and the features describing their visibility status dynamics. We further use this dataset to derive a boosted decision tree which winnows unintended LVPs with an accuracy of 95%.

DOI10.1109/INFOCOM.2014.6848023
Citation Keylutu2014separating