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.
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
.
DOI: 10.3969/j.issn.0255-8297.2025.03.006
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