Unsupervised real-time detection of BGP anomalies leveraging high-rate and fine-grained telemetry data

Recent technology evolution of network equipment allow to continuously stream a wealth of information, pertaining to multiple protocols and layers of the stack, at a very fine spatial-grain and at furthermore high-frequency. Processing this deluge of telemetry data in real-time clearly offers new opportunities for network control and troubleshooting, but also poses serious challenges. In this demonstration, we tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of BGP anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to telemetry features collected from a large-scale testbed comprising tens of routers traversed by 1 Terabit/s worth of real application traffic.