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An innovative approach for real-time network traffic classification

机译：实时网络流量分类的创新方法

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The growing demand for high-speed transmission rates in recent years attracted research in new mechanisms for network traffic characterization and classification. Their inadequate treatment degrades the performance of important operational schemes, such as Network Survivability, Traffic Engineering, Quality of Service (QoS), and Dynamic Access Control, among others. The most common methods for traffic classification are Deep Packet Inspection (DPI) and port based classification. However, those methods are becoming obsolete, as increasingly more traffic is being encrypted and applications are using dynamic ports or ports originally assigned to other popular applications. This paper presents a classification module for video streaming traffic, based on machine learning, as a solution for network schemes that require adequate real-time traffic treatment. The module adopts a new approach for the relaxation of the hypothesis of independence between the attributes of the Naive Bayes algorithm. The results show that the proposed module is a promising alternative to be applied in real-time scenarios. (C) 2019 Elsevier B.V. All rights reserved.
机译：近年来，对高速传输速率的需求不断增长，吸引了对网络流量表征和分类新机制的研究。它们的处理不当会降低重要运营方案的性能，例如网络生存能力，流量工程，服务质量（QoS）和动态访问控制等。流量分类最常用的方法是深度数据包检查（DPI）和基于端口的分类。但是，随着越来越多的流量被加密以及应用程序使用动态端口或最初分配给其他流行应用程序的端口，这些方法变得过时了。本文提出了一种基于机器学习的视频流流量分类模块，作为需要足够的实时流量处理的网络方案的解决方案。该模块采用了一种新方法来放宽朴素贝叶斯算法的属性之间的独立性假设。结果表明，所提出的模块是一种有前途的替代方案，可应用于实时场景。 （C）2019 Elsevier B.V.保留所有权利。

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