Encrypted traffic classification based on contrastive learning with spatial-temporal feature fusion

Rui Wang; Zhidong Wu; Yaxi Li; Feng Li; Siyuan Tian; Jianyi Liu

doi:10.1117/12.3025808

8 April 2024 Encrypted traffic classification based on contrastive learning with spatial-temporal feature fusion

Rui Wang, Zhidong Wu, Yaxi Li, Feng Li, Siyuan Tian, Jianyi Liu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309024 (2024) https://doi.org/10.1117/12.3025808
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviors through encryption, which brings new challenges to malicious traffic identification. How to effectively classify encrypted traffic without decrypting traffic and protecting user privacy has become an urgent problem to be solved. This paper proposes a classification method for encrypted traffic based on contrastive learning with spatial and temporal feature fusion. Unlike traditional contrastive learning with a single encoder, this method separately designs a spatial feature encoder and a temporal feature encoder to extract distinct features and implements contrastive learning in the same encoding layer through a fully connected layer to obtain a unique feature representation of encrypted traffic. This method focuses on the consistent features from different perspectives within the same sample. The effectiveness of this method is demonstrated through ablation experiments and comparisons with existing works.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Wang, Zhidong Wu, Yaxi Li, Feng Li, Siyuan Tian, and Jianyi Liu "Encrypted traffic classification based on contrastive learning with spatial-temporal feature fusion", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309024 (8 April 2024); https://doi.org/10.1117/12.3025808

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KEYWORDS

Feature extraction

Data modeling

Machine learning

Feature fusion

Education and training

Convolution

Image encryption

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