Flow-models 2.0: Elephant flows modeling and detection with machine learning

Abstract

This article presents the new version of the flow-models IP network flow modeling framework. The improved features include flow skipping and counting, flow filtering, IP address anonymization, and time series data calculation. The new version also enables simulation of the first packet mirroring feature and provides tools for modeling the detection of elephant flows. It includes examples of using the scikit-learn library to build machine learning models for elephant flow detection based on the first packet. Furthermore, it provides an anonymized flow dataset, enabling researchers to train and validate machine learning models for traffic analysis in a reproducible and comparable manner.

Keywords

• Elephant flows
• Mice flows
• Traffic engineering
• SDN
• Machine learning