Abstract
The Internet uses Border Gateway Protocol (BGP) for exchange of routes and reachability information between Autonomous Systems (AS). Hence, BGP is subject to anomalous traffic that can cause problems with connectivity and traffic loss. Routing Table Leak (RTL), worm and power outage events are considered anomalous in the sense that they can disrupt the Internet routing and cause slowdowns of varying severity, which leads to packet delivery reliability issues. Deep learning, a subfield of machine learning, could be applied in detection of BGP anomalies. Studying RTL, worm, and power outage events are of interest to network operators and researchers alike. In this paper, we consider datasets of several events, all of which caused large-scale Internet outages. We use artificial neural network (ANN) models based on a backpropagation algorithm for anomalous event classification.
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Cosovic, M., Obradovic, S., Junuz, E. (2018). Deep Learning for Detection of BGP Anomalies. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Time Series Analysis and Forecasting. ITISE 2017. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-96944-2_7
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