Enhancing Encrypted Traffic Classification with Deep Adaptation Networks-Reference-Cited by-同舟云学术

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Enhancing Encrypted Traffic Classification with Deep Adaptation Networks

Published:2023-10-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 48th Conference on Local Computer Networks (LCN)
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Author:

Dao Cuong¹,Tong Van²,Hoang Nam-Thang¹,Tran Hai-Anh¹,Tran Truong X.³

Affiliation:

1. Hanoi University of Civil Engineering,Faculty of Information Technology,Vietnam

2. Hanoi University of Science and Technology,School of Information and Communication Technology,Vietnam

3. Penn State University,School of Science, Engineering and Technology, Penn State Harrisburg,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10223299/10223313/10223333.pdf?arnumber=10223333

Reference11 articles.

1. Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

2. Malware Traffic Classification using Domain Adaptation and Ladder Network for Secure Industrial Internet of Things

3. Fast two-sample testing with analytic representations of probability measures;chwialkowski;Advances in neural information processing systems,2015

4. Machine Learning in Software Defined Networks: Data collection and traffic classification

5. Tracking User Application Activity by using Machine Learning Techniques on Network Traffic

Cited by 2 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. Continuous Select-and-Prune Incremental Learning for Encrypted Traffic Classification in Distributed SDN Networks;2024 IEEE 49th Conference on Local Computer Networks (LCN);2024-10-08

2. CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification;The Computer Journal;2024-02-26

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