论文标题
NETML：网络流量分析的挑战
NetML: A Challenge for Network Traffic Analytics
论文作者
论文摘要
分类网络流量是重要网络应用程序的基础。该领域的先前研究对代表性数据集的可用性面临着挑战，许多结果无法轻易复制。基于数据驱动的机器学习方法，这种问题加剧了这种问题。为了解决此问题，我们为研究社区提供了三个包含近130万个标签流的开放数据集，其中包含近130万个标签的流量和匿名的原始数据包。我们专注于网络流量分析中的广泛方面，包括恶意软件检测和应用程序分类。我们以称为NetML的开放挑战的形式发布数据集，并实现多种机器学习方法，包括随机孔，SVM和MLP。随着我们继续增长NetML，我们预计数据集将作为AI驱动的，可重现的网络流分析研究的共同平台。
Classifying network traffic is the basis for important network applications. Prior research in this area has faced challenges on the availability of representative datasets, and many of the results cannot be readily reproduced. Such a problem is exacerbated by emerging data-driven machine learning based approaches. To address this issue, we provide three open datasets containing almost 1.3M labeled flows in total, with flow features and anonymized raw packets, for the research community. We focus on broad aspects in network traffic analysis, including both malware detection and application classification. We release the datasets in the form of an open challenge called NetML and implement several machine learning methods including random-forest, SVM and MLP. As we continue to grow NetML, we expect the datasets to serve as a common platform for AI driven, reproducible research on network flow analytics.
