OADDS¶
OADDS is an Online Anomaly Detection engine for Data Streams capable of processing instances (data points) in a single-pass and unsupervised fashion. It uses the micro-clusters data structure presented in [DenStream].
The algorithm can be used to monitor Telemetry data (data streams) and raise alarms. Each instance is represented in the euclidean space as a 1 x F data point, where F is the number of features representing the measurements.
Demo¶
A demo of the first version of the algorithm [ACM SIGCOMM BigDama‘18] is available here: https://telemetry.telecom-paristech.fr/
References¶
[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,
[DenStream] Feng Cao, Martin Estert, Weining Qian, and Aoying Zhou, “Density-Based Clustering over an Evolving Data Stream with Noise” in Proceedings of the 2006 SIAM International Conference on Data Mining. 2006, 328-339
Features¶
- Free software: MIT license
- Documentation: https://oadds.readthedocs.io.
Credits¶
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.