基于机器学习和深度学习的网络流量分类研究

顾玥; 李丹; 高凯辉

doi:10.11959/j.issn.1000-0801.2021052

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基于机器学习和深度学习的网络流量分类研究

专题：内生安全 | 更新时间：2024-06-05

- 基于机器学习和深度学习的网络流量分类研究
- Research on network traffic classification based on machine learning and deep learning
- 电信科学 2021年37卷第3期页码：105-113
- 作者机构：
  
  1. 清华大学，北京 100084
  2. 清华大学深圳国际研究生院，广东深圳 518055
- 作者简介：
  
  [ "顾玥（1997- ），女，清华大学博士生，主要研究方向为智能网络。" ]
  [ "李丹（1981- ），男，清华大学计算机系教授、博士生导师，主要研究方向为数据中心网络、网络智能和可信任互联网。" ]
  [ "高凯辉（1996- ），男，清华大学深圳国际研究生院博士生，主要研究方向为性能可预测的数据中心网络和网络智能。" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Program of China(2018YFB1800500);广东省重点领域研发计划项目;Guangdong Provincial Research and Development Program(2018B010113001);国家自然科学基金资助项目;The National Natural Science Foundation of China(61772305);国家自然科学基金资助项目;The National Natural Science Foundation of China(61672499)
- DOI：10.11959/j.issn.1000-0801.2021052
  中图分类号： TP393
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-20
- 稿件说明：
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顾玥, 李丹, 高凯辉. 基于机器学习和深度学习的网络流量分类研究[J]. 电信科学, 2021,37(3):105-113.

Yue GU, Dan LI, Kaihui GAO. Research on network traffic classification based on machine learning and deep learning[J]. Telecommunications science, 2021, 37(3): 105-113.
顾玥, 李丹, 高凯辉. 基于机器学习和深度学习的网络流量分类研究[J]. 电信科学, 2021,37(3):105-113. DOI： 10.11959/j.issn.1000-0801.2021052.

Yue GU, Dan LI, Kaihui GAO. Research on network traffic classification based on machine learning and deep learning[J]. Telecommunications science, 2021, 37(3): 105-113. DOI： 10.11959/j.issn.1000-0801.2021052.

摘要

随着互联网技术的不断发展以及网络规模的不断扩大，应用的类别纷繁复杂，新型应用层出不穷。为了保障用户服务质量（QoS）并确保网络安全，准确快速的流量分类是运营商及网络管理者亟须解决的问题。首先给出网络流量分类的问题定义和性能指标；然后分别介绍基于机器学习和基于深度学习的流量分类方法，分析了这些方法的优缺点，并对现存问题进行阐述；接着围绕流量分类线上部署时会遇到的 3 个问题：数据集问题、新应用识别问题、部署开销问题对相关工作进行阐述与分析，并进一步探讨目前网络流量分类研究面临的挑战；最后对网络流量分类下一步的研究方向进行展望。

Abstract

With the continuous development of Internet technology and the continuous expansion of network scale

there are many different types of applications

and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security

accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly

the problem definition and performance metrics of network traffic classification were given.Then

the traffic classification methods based on machine learning and deep learning were introduced respectively

the advantages and disadvantages of these methods were analyzed

and the existing problems were expounded.Next

the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment: dataset

zero-day application identification and the cost of online deployment

and further discusses the challenges faced by the current network traffic classification researches.Finally

the next research direction of network traffic classification was prospected.

关键词

Keywords

references

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