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Online classification of user activities using machine learning on network traffic

机译：在网络流量上使用机器学习的用户活动在线分类

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摘要

The daily deployment of new applications, along with the exponential increase in network traffic, entails a growth in the complexity of network analysis and monitoring. Conversely, the increasing availability and decreasing cost of computational capacity have increased the popularity and usability of machine learning algorithms. In this paper, a system for classifying user activities from network traffic using both supervised and unsupervised learning is proposed. The system uses the behaviour exhibited over the network and classifies the underlying user activity, taking into consideration all of the traffic generated by the user within a given time window. Those windows are characterised with features extracted from the network and transport layer headers in the traffic flows. A three-layer model is proposed to perform the classification task. The first two layers of the model are implemented using K-Means, while the last one uses a Random Forest to obtain the activity labels. An average accuracy of 97.37% is obtained, with values of precision and recall that allow online classification of network traffic for Quality of Service (QoS) and user profiling, outperforming previous proposals.
机译：每日部署新应用程序，以及网络流量的指数增加，需要增加网络分析和监测的复杂性。相反，计算能力的增加的可用性和降低成本增加了机器学习算法的普及和可用性。在本文中，提出了一种用于使用监督和无监督学习的网络流量对用户活动进行分类的系统。系统使用网络上展出的行为，并考虑到在给定时间窗口内由用户生成的所有流量来分类基础用户活动。这些Windows的特征在于从网络中提取的功能和流量流中的传输层标题。提出了一种三层模型来执行分类任务。模型的前两层使用k均值来实现，而最后一个使用随机林以获得活动标签。获得97.37％的平均精度，具有精度和回忆的值，允许在线分类网络流量以获得服务质量（QoS）和用户分析，优于上一个提案。

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  • 来源
    《Computer networks》 |2020年第9期|107557.1-107557.12|共12页
  • 作者

    Labayen Victor; Magana Eduardo; Morato Daniel; Izal Mikel;

  • 作者单位

    Naudit High Performance Comp & Networking SL Madrid Spain;

    Univ Publ Navarra Dept Elect Elect & Commun Engn Pamplona Spain;

    Univ Publ Navarra Dept Elect Elect & Commun Engn Pamplona Spain;

    Univ Publ Navarra Dept Elect Elect & Commun Engn Pamplona Spain;

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