TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification-Reference-Cited by-同舟云学术

TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2023
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Author:

Zhang Haozhen¹^ORCID,Yu Le²^ORCID,Xiao Xi¹^ORCID,Li Qing³^ORCID,Mercaldo Francesco⁴^ORCID,Luo Xiapu²^ORCID,Liu Qixu⁵^ORCID

Affiliation:

1. Shenzhen International Graduate School, Tsinghua University, China

2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong

3. Peng Cheng Laboratory, China

4. University of Molise, Italy and IIT-CNR, Italy

5. Institute of Information Engineering, Chinese Academy of Sciences, China

Funder

National Natural Science Foundation of China

Research and Development Program of Shenzhen

Overseas Research Cooperation Fund of Tsinghua Shenzhen International Graduate School

HK ITF Project

Shenzhen Science and Technology Innovation Commission: Research Center for Computer Network (Shenzhen) Ministry of Education

the Key Laboratory of Network Assessment Technology, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583227

Reference47 articles.

1. Wei Cai , Gaopeng Gou , Minghao Jiang , Chang Liu , Gang Xiong , and Zhen Li . 2021 . MEMG: Mobile Encrypted Traffic Classification With Markov Chains and Graph Neural Network. In IEEE International Conference on High Performance Computing and Communications. 478–486 . Wei Cai, Gaopeng Gou, Minghao Jiang, Chang Liu, Gang Xiong, and Zhen Li. 2021. MEMG: Mobile Encrypted Traffic Classification With Markov Chains and Graph Neural Network. In IEEE International Conference on High Performance Computing and Communications. 478–486.

2. Weilin Cong , Morteza Ramezani , and Mehrdad Mahdavi . 2021 . On Provable Benefits of Depth in Training Graph Convolutional Networks. In Conference on Neural Information Processing Systems. 9936–9949 . Weilin Cong, Morteza Ramezani, and Mehrdad Mahdavi. 2021. On Provable Benefits of Depth in Training Graph Convolutional Networks. In Conference on Neural Information Processing Systems. 9936–9949.

3. Analyzing Android Encrypted Network Traffic to Identify User Actions

4. A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis

5. Gerard Drapper Gil , Arash Habibi Lashkari , Mohammad Mamun , and Ali A. Ghorbani . 2016 . Characterization of Encrypted and VPN Traffic Using Time-Related Features. In International Conference on Information Systems Security and Privacy. 407–414 . Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, and Ali A. Ghorbani. 2016. Characterization of Encrypted and VPN Traffic Using Time-Related Features. In International Conference on Information Systems Security and Privacy. 407–414.

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