A Novel Self-Supervised Framework Based on Masked Autoencoder for Traffic Classification-Reference-Cited by-同舟云学术

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A Novel Self-Supervised Framework Based on Masked Autoencoder for Traffic Classification

Published:2024-06 Issue:3 Volume:32 Page:2012-2025
ISSN:1063-6692
Container-title:IEEE/ACM Transactions on Networking
language:
Short-container-title:IEEE/ACM Trans. Networking

Author:

Zhao Ruijie¹^ORCID,Zhan Mingwei¹,Deng Xianwen¹^ORCID,Li Fangqi¹^ORCID,Wang Yanhao²^ORCID,Wang Yijun¹^ORCID,Gui Guan³^ORCID,Xue Zhi¹^ORCID

Affiliation:

1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. QI-ANXIN Technology Research Institute, Beijing, China

3. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

Funder

Shanghai Jiao Tong University (SJTU)-QI'ANXIN Joint Laboratory of Information System Security

National Key Research and Development Program of China

NIO Student Innovation Funding Program

Network Security & Ecosystem Innovation Laboratory of Intelligent Radio & Television Broadcasting of the National Radio and Television Administration

Thirty-Seventh AAAI Conference on Artificial Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/90/10559910/10380504.pdf?arnumber=10380504

Reference42 articles.

1. Yet Another Traffic Classifier: A Masked Autoencoder Based Traffic Transformer with Multi-Level Flow Representation

2. A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures

3. MT-FlowFormer: A Semi-Supervised Flow Transformer for Encrypted Traffic Classification

4. NMMF-Stream: A Fast and Accurate Stream-Processing Scheme for Network Monitoring Data Recovery

5. Middlebox-Based Packet-Level Redundancy Elimination Over Encrypted Network Traffic

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