We are happy to share with the community our latest effort on unsupervised stream learning of network anomalies!
We are releasing all code and dataset of our recent ACM SIGCOMM BigDAMA’18 and IEEE INFOCOM publications, in particular
- our open source implementation of a network anomaly detection algorithms based on DenStream, an unsupervised stream learning clustering algorithm
- our real-world datasets, from a state of the art BGP data-center with over 1Tbps aggregated traffic
- our online demonstration, visualizing the algorithm in action telemetry-based stream-learning of BGP anomalies
- the necessary scripts to reproduce our scientific results
[ACM SIGCOMM BigDama’18] Putina, Andrian and Rossi, Dario and Bifet, Albert and Barth, Steven and Pletcher, Drew and Precup, Cristina and Nivaggioli, Patrice, Telemetry-based stream-learning of BGP anomalies ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (Big-DAMA’18) aug. 2018
[IEEE INFOCOM’18] Putina, Andrian and Rossi, Dario and Bifet, Albert and Barth, Steven and Pletcher, Drew and Precup, Cristina and Nivaggioli, Patrice, Unsupervised real-time detection of BGP anomalies leveraging high-rate and fine-grained telemetry data IEEE INFOCOM, Demo Session apr. 2018,