Active Learning Framework to Automate NetworkTraffic Classification

TL;DR

This paper introduces a novel Active Learning Framework (ALF) for network traffic classification that continuously updates datasets and models, addressing challenges like data scarcity and high traffic volumes in high-speed networks.

Contribution

The paper presents a new ALF that automates dataset and model evolution for network traffic analysis, supporting high-speed networks and research experimentation.

Findings

ALF can be deployed for 100 Gb/s network analysis.

Supports research on annotation and dataset optimization.

Addresses dataset aging and data scarcity challenges.

Abstract

Recent network traffic classification methods benefitfrom machine learning (ML) technology. However, there aremany challenges due to use of ML, such as: lack of high-qualityannotated datasets, data-drifts and other effects causing aging ofdatasets and ML models, high volumes of network traffic etc. Thispaper argues that it is necessary to augment traditional workflowsof ML training&deployment and adapt Active Learning concepton network traffic analysis. The paper presents a novel ActiveLearning Framework (ALF) to address this topic. ALF providesprepared software components that can be used to deploy an activelearning loop and maintain an ALF instance that continuouslyevolves a dataset and ML model automatically. The resultingsolution is deployable for IP flow-based analysis of high-speed(100 Gb/s) networks, and also supports research experiments ondifferent strategies and methods for annotation, evaluation, datasetoptimization, etc. Finally, the paper lists some research challengesthat emerge from the first experiments with ALF in practice.