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TrafficMAE: A network traffic classification model based on masked autoencoder
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DOI：10.1016/j.icte.2025.11.004.png)
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Abstract
En 中文
Network traffic classification is critical for security and performance. Encrypted protocols challenge traditional methods, and supervised approaches suffer from limited labeled data. We propose TrafficMAE, a masked autoencoder using a Session Video representation to encode temporal and hierarchical traffic information. Our self-supervised pre-training combines masked reconstruction with bidirectional packet direction prediction to learn from unlabeled data. Fine-tuning fuses statistical and learned features. Evaluations on five tasks show TrafficMAE outperforms existing methods with superior F1-scores, strong few-shot performance, and better efficiency compared to transformer-based baselines.
Keywords:
Network traffic classification
Masked autoencoder
Encrypted traffic analysis
Deep learning
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