基于对比学习的半监督加密流量分类模型

doi:10.3969/j.issn.0255-8297.2025.03.006

Abstract

Abstract: To address the performance degradation of most encrypted traffic classification (ETC) models due to scarce labeled data, this paper proposes a semi-supervised encrypted traffic classification model based on contrastive learning (SSETC-CL). By comparing the similarities and differences between samples, SSETC-CL is capable of learning useful representations from large amounts of unlabeled data, thereby obtaining a versatile and effective feature encoding network, and reducing dependence on labeled data for downstream tasks. The performance of SSETC-CL is evaluated on the public dataset ISCXVPN2016 as well as two self-collected datasets. Compared to other baseline models, SSETC-CL achieved a maximum accuracy improvement of 8.92% on the specified task, showing its superior performance. Experimental results clearly demonstrate that SSETC-CL not only achieves high accuracy on traffic seen during pretraining but also exhibits the ability to transfer the knowledge gained from pretraining to unknown traffic.

Key words: encrypted traffic classification (ETC), contrastive learning, semi-supervised, data augmentation, transfer learning

CLC Number:

P751.1

JIN Yanliang, FANG Jie, GAO Yuan, ZHOU Jiahao. Semi-supervised Encrypted Traffic Classification Model Based on Contrastive Learning[J]. Journal of Applied Sciences, 2025, 43(3): 437-450.

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Semi-supervised Encrypted Traffic Classification Model Based on Contrastive Learning

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