EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks-Reference-Cited by-同舟云学术

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EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks

Published:2023-04 Issue: Volume:224 Page:109614
ISSN:1389-1286
Container-title:Computer Networks
language:en
Short-container-title:Computer Networks

Author:

Diao Zulong^ORCID,Xie Gaogang,Wang Xin,Ren Rui,Meng Xuying,Zhang Guangxing,Xie Kun,Qiao Mingyu

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference36 articles.

1. Internet Assigned Numbers Authority (IANA) Considerations for the Lightweight Directory Access Protocol (LDAP);Zeilenga,2006

2. Identifying known and unknown peer-to-peer traffic;Constantinou,2006

3. Lightweight, payload-based traffic classification: An experimental evaluation;Risso,2008

4. PiDFA: A practical multi-stride regular expression matching engine based on FPGA;Yang,2016

5. Speeding-up DPI traffic classification with chaining;Doroud,2018

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1. Online network traffic classification based on external attention and convolution by IP packet header;Computer Networks;2024-10

2. Deep learning and pre-training technology for encrypted traffic classification: A comprehensive review;Neurocomputing;2024-08

3. Network traffic classification based‐ masked language regression model using CNN;Concurrency and Computation: Practice and Experience;2024-07-15

4. Packet header-based reweight-long short term memory (Rew-LSTM) method for encrypted network traffic classification;Computing;2024-07-02

5. Malicious Traffic Detection in DNS Over HTTPS (DoH): Edge Prediction with Graph Convolutional Network;2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC);2024-07-02

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