RM-Net: An Encrypted Traffic Classification Method Based on Deep Learning for Low Labeled Rate Scenarios-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

RM-Net: An Encrypted Traffic Classification Method Based on Deep Learning for Low Labeled Rate Scenarios

Published:2023-11-17 Issue: Volume:77 Page:4399-4404
ISSN:
Container-title:2023 China Automation Congress (CAC)
language:
Short-container-title:

Author:

Yang Xu¹,Zhang Kaisa¹,Chuai Gang¹,Chen Zibin²,Chen Xiangyu¹,Xiong Zihao¹,Xu Yichao¹

Affiliation:

1. Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China,100876

2. University of Massachusetts,Department of Electrical and Computer Engineering,Amherst,MA,USA,01003

Funder

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10450150/10449968/10450905.pdf?arnumber=10450905

Reference14 articles.

1. A survey of techniques for internet traffic classification using machine learning

2. Internet Traffic Detection using Naïve Bayes and K-Nearest Neighbors (KNN) algorithm

3. Traffic classification on the fly

4. Flow Clustering Using Machine Learning Techniques

5. Network Anomaly Detection Using Random Forests and Entropy of Traffic Features

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP