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。

自动化网络流量分类的主动学习框架

Active Learning Framework to Automate NetworkTraffic Classification

AutoAugment has sparked an interest in automated augmentation methods for
deep learning models. These methods estimate image transformation policies for
train data that improve generalization to test data. While recent papers
evolved in the direction of decreasing policy search complexity, we show that
those methods are not robust when applied to biased and noisy data. To overcome
these limitations, we reformulate AutoAugment as a generalized automated
dataset optimization (AutoDO) task that minimizes the distribution shift
between test data and distorted train dataset. In our AutoDO model, we
explicitly estimate a set of per-point hyperparameters to flexibly change
distribution of train data. In particular, we include hyperparameters for
augmentation, loss weights, and soft-labels that are jointly estimated using
implicit differentiation. We develop a theoretical probabilistic interpretation
of this framework using Fisher information and show that its complexity scales
linearly with the dataset size. Our experiments on SVHN, CIFAR-10/100, and
ImageNet classification show up to 9.3% improvement for biased datasets with
label noise compared to prior methods and, importantly, up to 36.6% gain for
underrepresented SVHN classes.

AutoDO 可用于深度学习模型的自动数据扩充，通过对损失权重、软标签等超参数进行联合估计来最小化测试数据和失真的训练数据之间的分布偏移，实现了偏差数据和标签噪音下的机器学习模型优化，相较于之前的方法，有着多达 9.3% 的准确度提升，并且在 SVHN 分类优化上达到了 36.6% 的增益。