ET-Former: Robust Transformer-Based Representation for Encrypted Traffic Classification

Encrypted traffic classification requires capturing robust and effective traffic representations from data that lack explicit patterns and clear semantics, which is crucial for network management and cybersecurity. Existing methods heavily rely on large amounts of labeled data or expert-designed features and struggle to generalize across different classification scenarios. Leveraging unlabeled traffic data to learn universal representations of encrypted traffic remains a key challenge. In this paper, we propose a novel traffic representation model called ET-Former. ET-Former learns universal representations of various encrypted traffic from large-scale unlabeled data and can be fine-tuned with a small amount of labeled data for specific tasks. ET-Former achieves state-of-the-art performance in four of five encrypted traffic classification tasks, demonstrating exciting features such as robustness, generalization, and accuracy.