A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification-Reference-Cited by-同舟云学术

A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification

Published:2022-05-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
language:
Short-container-title:

Author:

Lin Kunda¹,Xu Xiaolong¹,Jiang Yu¹

Affiliation:

1. School of Computer Science Nanjing University of Posts and Telecommunications,Jiangsu Key Laboratory of Big Data Security & Intelligent Processing,Nanjing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9775971/9776016/09776310.pdf?arnumber=9776310

Reference29 articles.

1. Clustering to Assist Supervised Machine Learning for Real-Time IP Traffic Classification

2. An improved network traffic classification algorithm based on Hadoop decision tree

3. An innovative approach for real-time network traffic classification

4. Accurate Classification of the Internet Traffic Based on the SVM Method

5. A Performance Study of Hidden Markov Model and Random Forest in Internet Traffic Classification

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MT-CNN: A Classification Method of Encrypted Traffic Based on Semi-Supervised Learning;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04

2. Tabular-based self-supervised learning approach for encrypted traffic classification;Journal of Electronic Imaging;2023-08-21

3. Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies;Mathematical Biosciences and Engineering;2023