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Custom implementation of the DenStream algorithm in Python. The purpose is to detect anomalies applying the algorithm on Telemetry data coming from the devices.

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anrputina/ods-anomalydetection

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OADDS

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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.

Related repositories

The code released in this website are also instrumental to reproduce results that are published in [ACM SIGCOMM BigDama'18] and that are demonstrated at [IEEE INFOCOM'18] (see the Reference section below)

This repository only contains the algorithm, whereas related repositories contain - the datasets we released to the community - specific instruction and code to replicate the paper results https://github.com/anrputina/OutlierDenStream-BigDama18

Demo

A demo of the first version of the algorithm [ACM SIGCOMM BigDama'18] is available here: https://telemetry.telecom-paristech.fr/

Dataset

The Dataset in use are extracted from here

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

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This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

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Custom implementation of the DenStream algorithm in Python. The purpose is to detect anomalies applying the algorithm on Telemetry data coming from the devices.

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