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.

本文介绍了一种基于主动学习框架（ALF）的网络流量分类方法，它提供了准备好的软件组件，用于部署主动学习循环和自动维护数据集和机器学习模型的实例。该方法可应用于高速网络（100Gb/s），支持不同的注释、评估和数据集优化策略的研究实验。