ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification

By Xinjie Lin, Gang Xiong, Gaopeng Gou, Zhen Li, Junzheng Shi, Jiangtao Yu. The Web Conference, 2022

572 citations recorded by the source corpus.

Encrypted traffic classification requires discriminative and robust traffic representation captured from content-invisible and imbalanced traffic data for accurate classification, which is challenging but indispensable to achieve network security and network management. The major limitation of existing solutions is that they highly rely on the deep features, which are overly dependent on data size and hard to generalize on unseen data. How to leverage the open-domain unlabeled traffic data to learn representation with strong generalization ability remains a key challenge. In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data. The pre-trained model can be fine-tuned on a small number of task-specific labeled data and achieves state-of-the-art performance across five encrypted traffic classification tasks, remarkably pushing the F1 of ISCX-VPN-Service to 98.9% (5.2%↑), Cross-Platform (Android) to 92.5% (5.4%↑), CSTNET-TLS 1.3 to 97.4% (10.0%↑). Notably, we provide explanation of the empirically powerful pre-training model by analyzing the randomness of ciphers. It gives us insights in understanding the boundary of classification ability over encrypted traffic. The code is available at: https://github.com/linwhitehat/ET-BERT.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/29ed68d701f8450853938827b5124c9613c56aff

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  1. Haolun Wu
  2. Chengming Hu
  3. Dun Yuan
  4. Can Chen
  5. Xue Liu
  6. Ye Yuan
  7. C. Zhang
  8. Xianbin Wang

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