基于人工智能技术的互联网流量分类研究进展

Mohammad Pooya Malek, Shaghayegh Naderi, Hossein Gharaee Garakani
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引用次数: 0

摘要

几乎每个行业都因人工智能而发生了革命性的变化。电信行业是其中之一,通过增强网络基础设施能力来提高客户的服务质量和体验质量,即使在5G网络中也可能带来更高的费率。为此,使用网络流分类方法对用户行为进行识别和分类。在第四次工业革命之前,基于统计、基于端口、基于有效载荷和基于流量的传统分析方法是这些系统的关键。人工智能与这些方法相结合,精度更高,性能更好。在过去的几十年里,人们对机器学习和深度学习进行了大量的研究,但仍然存在一些关于使用深度学习而不是ML或反之亦然的疑问。本文试图通过探索140多个相同的研究来研究ML/DL用例中的挑战。然后,我们分析了结果,并可视化了一种实用的方法来为流行的应用程序分类互联网流量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review on Internet Traffic Classification Based on Artificial Intelligence Techniques
—Almost every industry has revolutionized with Artificial Intelligence. The telecommunication industry is one of them to improve customers' Quality of Services and Quality of Experience by enhancing networking infrastructure capabilities which could lead to much higher rates even in 5G Networks. To this end, network traffic classification methods for identifying and classifying user behavior have been used. Traditional analysis with Statistical-Based, Port-Based, Payload-Based, and Flow-Based methods was the key for these systems before the 4th industrial revolution. AI combination with such methods leads to higher accuracy and better performance. In the last few decades, numerous studies have been conducted on Machine Learning and Deep Learning, but there are still some doubts about using DL over ML or vice versa. This paper endeavors to investigate challenges in ML/DL use-cases by exploring more than 140 identical researches. We then analyze the results and visualize a practical way of classifying internet traffic for popular applications.
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