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A Survey on Feature Selection Techniques for Internet Traffic Classification

机译：互联网流量分类特征选择技术研究

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摘要

Feature selection technique has a great importance in Internet traffic classification. Machine learning (ML) algorithms have been generally applied in novel traffic classification. In this paper we provide an overview of three major approaches to classify different categories of Internet traffic: Port based approach, Payload based approach, Statistical-based approach. This paper also explain feature selection algorithms, which are classified into 3 methods: Filter method, Wrapper method, Embedded Method along with their benefits and limitations and also provides an overview of some of the feature selection technique present in literature. The aim of the survey gives a brief idea about feature selection techniques which can be applied to many machine learning algorithms to avoid problems like class imbalance, concept drift, low efficiency, and low classification rate etc.
机译：特征选择技术在互联网流量分类中具有非常重要的意义。机器学习（ML）算法已普遍应用于新颖的流量分类中。在本文中，我们提供了三种主要方法来对Internet流量的不同类别进行分类：基于端口的方法，基于有效负载的方法，基于统计的方法。本文还介绍了特征选择算法，它们分为3种方法：过滤器方法，包装器方法，嵌入式方法，以及它们的优点和局限性，并概述了文献中存在的一些特征选择技术。调查的目的是简要介绍有关特征选择技术的知识，这些技术可以应用于许多机器学习算法，以避免类不平衡，概念漂移，效率低和分类率低等问题。

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