Active Learning Framework to Automate NetworkTraffic Classification

• URL: http://arxiv.org/abs/2211.08399v1
• Date: Wed, 26 Oct 2022 10:15:18 GMT
• Title: Active Learning Framework to Automate NetworkTraffic Classification
• Authors: Jaroslav Pe\v{s}ek, Dominik Soukup, Tom\'a\v{s} \v{C}ejka
• Abstract summary: The paper presents a novel ActiveLearning Framework (ALF) to address this topic. ALF provides components that can be used to deploy an activelearning loop and maintain an ALF instance that continuouslyevolves a dataset and ML model. The resultingsolution is deployable for IP flow-based analysis of high-speed(100 Gb/s) networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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