ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification-Reference-Cited by-同舟云学术

ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
language:
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Author:

Lin Xinjie¹,Xiong Gang¹,Gou Gaopeng¹,Li Zhen¹,Shi Junzheng²,Yu Jing¹

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences, China and School of Cyber Security, University of Chinese Academy of Sciences, China

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

Funder

the Strategic Priority Research Program of Chinese Academy of Sciences

The National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512217

Reference42 articles.

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2. Emily Alsentzer , John R. Murphy , Willie Boag , Wei-Hung Weng , Di Jin , Tristan Naumann , and Matthew B . A. McDermott. 2019 . Publicly Available Clinical BERT Embeddings. In the Clinical Natural Language Processing Workshop. 72–78 . Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew B. A. McDermott. 2019. Publicly Available Clinical BERT Embeddings. In the Clinical Natural Language Processing Workshop. 72–78.

3. Amazon. 2021. Alexa Top Sites. https://www.alexa.com/topsites/. Amazon. 2021. Alexa Top Sites. https://www.alexa.com/topsites/.

4. Independent comparison of popular DPI tools for traffic classification

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