Research on the Application of Transformer in Encrypted Traffic Recognition

Sheng Hong; Xinyan Gao; Xiaohu Chen; Duanni Meng; Shuang Gu

doi:10.1109/MLCCIM60412.2023.00075

Research on the Application of Transformer in Encrypted Traffic Recognition

Sheng Hong^*
, Xinyan Gao
, Xiaohu Chen
, Duanni Meng
, Shuang Gu

^*Corresponding author for this work

School of Cyber Science and Technology

Beihang University
Hubei China Tobacco Industry Co.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Encrypted traffic recognition is a crucial research area in network security. However, traditional recognition methods cannot meet practical needs due to the concealment and diversity of encrypted traffic. The emergence of deep learning techniques provides new ideas for encrypted traffic recognition. As a neural network model based on the attention mechanism, transformer exhibits excellent sequence modeling ability and has achieved successful applications in various domains. In this paper, we introduce the current research status of applying transformer in the field of encrypted traffic recognition, analyze the technical development line, propose the framework of encrypted traffic recognition built upon hybrid learning, and look forward to the development direction of transformer in the domain of encrypted traffic recognition, so as to provide reference and reference for further improving the accuracy and practicality of encrypted traffic recognition.

Original language	English
Title of host publication	Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	468-473
Number of pages	6
ISBN (Electronic)	9798350328530
DOIs	https://doi.org/10.1109/MLCCIM60412.2023.00075
State	Published - 2023
Event	2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023 - Jiuzhaigou, China Duration: 28 Aug 2023 → 31 Aug 2023

Publication series

Name	Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

Conference

Conference	2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023
Country/Territory	China
City	Jiuzhaigou
Period	28/08/23 → 31/08/23

Keywords

deep learning
encrypted traffic recognition
transformer

Access to Document

10.1109/MLCCIM60412.2023.00075

Cite this

Hong, S., Gao, X., Chen, X., Meng, D., & Gu, S. (2023). Research on the Application of Transformer in Encrypted Traffic Recognition. In Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023 (pp. 468-473). (Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MLCCIM60412.2023.00075

Hong, Sheng ; Gao, Xinyan ; Chen, Xiaohu et al. / Research on the Application of Transformer in Encrypted Traffic Recognition. Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 468-473 (Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023).

@inproceedings{275eca6184094f07b3d93e5dcd9fc90b,

title = "Research on the Application of Transformer in Encrypted Traffic Recognition",

abstract = "Encrypted traffic recognition is a crucial research area in network security. However, traditional recognition methods cannot meet practical needs due to the concealment and diversity of encrypted traffic. The emergence of deep learning techniques provides new ideas for encrypted traffic recognition. As a neural network model based on the attention mechanism, transformer exhibits excellent sequence modeling ability and has achieved successful applications in various domains. In this paper, we introduce the current research status of applying transformer in the field of encrypted traffic recognition, analyze the technical development line, propose the framework of encrypted traffic recognition built upon hybrid learning, and look forward to the development direction of transformer in the domain of encrypted traffic recognition, so as to provide reference and reference for further improving the accuracy and practicality of encrypted traffic recognition.",

keywords = "deep learning, encrypted traffic recognition, transformer",

author = "Sheng Hong and Xinyan Gao and Xiaohu Chen and Duanni Meng and Shuang Gu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023 ; Conference date: 28-08-2023 Through 31-08-2023",

year = "2023",

doi = "10.1109/MLCCIM60412.2023.00075",

language = "英语",

series = "Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "468--473",

booktitle = "Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023",

address = "美国",

}

Hong, S, Gao, X, Chen, X, Meng, D & Gu, S 2023, Research on the Application of Transformer in Encrypted Traffic Recognition. in Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023. Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 468-473, 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023, Jiuzhaigou, China, 28/08/23. https://doi.org/10.1109/MLCCIM60412.2023.00075

Research on the Application of Transformer in Encrypted Traffic Recognition. / Hong, Sheng; Gao, Xinyan; Chen, Xiaohu et al.
Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 468-473 (Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on the Application of Transformer in Encrypted Traffic Recognition

AU - Hong, Sheng

AU - Gao, Xinyan

AU - Chen, Xiaohu

AU - Meng, Duanni

AU - Gu, Shuang

PY - 2023

Y1 - 2023

N2 - Encrypted traffic recognition is a crucial research area in network security. However, traditional recognition methods cannot meet practical needs due to the concealment and diversity of encrypted traffic. The emergence of deep learning techniques provides new ideas for encrypted traffic recognition. As a neural network model based on the attention mechanism, transformer exhibits excellent sequence modeling ability and has achieved successful applications in various domains. In this paper, we introduce the current research status of applying transformer in the field of encrypted traffic recognition, analyze the technical development line, propose the framework of encrypted traffic recognition built upon hybrid learning, and look forward to the development direction of transformer in the domain of encrypted traffic recognition, so as to provide reference and reference for further improving the accuracy and practicality of encrypted traffic recognition.

AB - Encrypted traffic recognition is a crucial research area in network security. However, traditional recognition methods cannot meet practical needs due to the concealment and diversity of encrypted traffic. The emergence of deep learning techniques provides new ideas for encrypted traffic recognition. As a neural network model based on the attention mechanism, transformer exhibits excellent sequence modeling ability and has achieved successful applications in various domains. In this paper, we introduce the current research status of applying transformer in the field of encrypted traffic recognition, analyze the technical development line, propose the framework of encrypted traffic recognition built upon hybrid learning, and look forward to the development direction of transformer in the domain of encrypted traffic recognition, so as to provide reference and reference for further improving the accuracy and practicality of encrypted traffic recognition.

KW - deep learning

KW - encrypted traffic recognition

KW - transformer

UR - https://www.scopus.com/pages/publications/85182029322

U2 - 10.1109/MLCCIM60412.2023.00075

DO - 10.1109/MLCCIM60412.2023.00075

M3 - 会议稿件

AN - SCOPUS:85182029322

T3 - Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

SP - 468

EP - 473

BT - Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

Y2 - 28 August 2023 through 31 August 2023

ER -

Hong S, Gao X, Chen X, Meng D, Gu S. Research on the Application of Transformer in Encrypted Traffic Recognition. In Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 468-473. (Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023). doi: 10.1109/MLCCIM60412.2023.00075

Research on the Application of Transformer in Encrypted Traffic Recognition

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this