基于机器学习的网络流量分类研究

Research on Network Traffic Classification Based on Machine Learning

随着互联网的高速发展,网络流量在数量上和复杂程度上都在迅速增长。如何能快速准确地识别出网络流量类型,已经成为计算机网络领域研究的热点。流量分类是网络流量管理的基础,其目的是识别和分类不同类型的流量。目前,基于机器学习进行流量分类是一种非常有前途的技术。机器学习算法可以从大量数据中学习特征,将其应用于网络流量,可以提高分类的准确性,可帮助网络管理人员为人们提供更好的上网体验。本文首先介绍了网络流量分类的基本概念和传统的网络流量分类方法,然后介绍了两种基于机器学习的网络流量算法。

With the rapid development of the Internet,network traffic is growing rapidly in quantity and complexity.How to quickly and accurately identify the type of network traffic has become a hot spot in the field of computer networks.Traffic classification is the foundation of network traffic management,and its purpose is to identify and classify different types of traffic.Currently,traffic classification based on machine learning is a very promising technique.Machine learning algorithms can learn features from large amounts of data and apply them to network traffic,which can improve the accuracy of classification and help network managers provide people with a better online experience.This article first introduces the basic concepts of network traffic classification and traditional network traffic classification methods,and then introduces two network traffic algorithms based on machine learning.