论文标题
主动学习框架以自动化网络施工分类
Active Learning Framework to Automate NetworkTraffic Classification
论文作者
论文摘要
最近的网络流量分类方法受益于机器学习（ML）技术。但是，由于使用ML而引起的挑战，例如：缺乏高质量的数据集，数据拖船和其他效果，导致衰老和ML模型，网络流量的大量。本文纸张认为，需要增强传统的ML ML培训和部署的传统工作流量，并适应了Aptims Learning Convacton网络流量分析是必要的。该论文提出了一个新颖的ActiveLearning框架（ALF）来解决此主题。 ALF提供了准备好的软件组件，可用于部署ActiveLearning循环并维护ALF实例，该实例可以自动连续验证数据集和ML模型。结果可用于基于IP流的高速（100 GB/s）网络的分析，还支持研究实验，以注释，评估，数据量限制等各个不同的策略和方法。最后，本文列出了一些研究挑战的挑战，其中一些研究的挑战是在实践中与ALF的第一个实验中提出的。
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.
