Open Access iconOpen Access

ARTICLE

crossmark

MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN

Kaiyue Wang1, Jian Gao1,2,*, Xinyan Lei1

1 Department of Information Network Security, People’s Public Security University of China, Beijing, 102600, China
2 Safety Precaution Laboratory of Ministry of Public Security, Beijing, 102600, China

* Corresponding Author: Jian Gao. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 619-638. https://doi.org/10.32604/iasc.2023.036701

Abstract

Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed (MTC). In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences, the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block. This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences. The model simultaneously handles multiple tasks, which lowers the cost of training. Experiments reveal that on the ISCX VPN-nonVPN dataset, the model achieves an average F1 score of 98.25% and an average recall of 98.30% for the task of identifying applications, and an average F1 score of 97.94%, and an average recall of 97.54% for the task of traffic characterization. When advanced models on the same dataset are chosen for comparison, the model produces the best results. To prove the generalization, we applied MTC to CICIDS2017 dataset, and our model also achieved good results.

Keywords


Cite This Article

APA Style
Wang, K., Gao, J., Lei, X. (2023). MTC: A multi-task model for encrypted network traffic classification based on transformer and 1D-CNN. Intelligent Automation & Soft Computing, 37(1), 619-638. https://doi.org/10.32604/iasc.2023.036701
Vancouver Style
Wang K, Gao J, Lei X. MTC: A multi-task model for encrypted network traffic classification based on transformer and 1D-CNN. Intell Automat Soft Comput . 2023;37(1):619-638 https://doi.org/10.32604/iasc.2023.036701
IEEE Style
K. Wang, J. Gao, and X. Lei "MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN," Intell. Automat. Soft Comput. , vol. 37, no. 1, pp. 619-638. 2023. https://doi.org/10.32604/iasc.2023.036701



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1830

    View

  • 931

    Download

  • 0

    Like

Share Link