ET-BERT

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The repository of ET-BERT, a network traffic classification model on encrypted traffic.

ET-BERT is a method for learning datagram contextual relationships from encrypted traffic, which could be directly applied to different encrypted traffic scenarios and accurately identify classes of traffic. First, ET-BERT employs multi-layer attention in large scale unlabelled traffic to learn both inter-datagram contextual and inter-traffic transport relationships. Second, ET-BERT could be applied to a specific scenario to identify traffic types by fine-tuning the labeled encrypted traffic on a small scale.

The work is introduced in the 31st The Web Conference:

Xinjie Lin, Gang Xiong, Gaopeng Gou, Zhen Li, Junzheng Shi and Jing Yu. 2022. ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification. In Proceedings of The Web Conference (WWW) 2022, Lyon, France. Association for Computing Machinery.

Note: this code is based on UER-py. Many thanks to the authors.

Table of Contents

• Requirements
• Datasets
• Using ET-BERT
• Reproduce ET-BERT
• Citation
• Contact

Requirements

• Python >= 3.6
• CUDA: 11.4
• GPU: Tesla V100S
• torch >= 1.1
• six >= 1.12.0
• scapy == 2.4.4
• numpy == 1.19.2
• shutil, random, json, pickle, binascii, flowcontainer
• argparse
• packaging
• tshark
• SplitCap
• scikit-learn
• For the mixed precision training you will need apex from NVIDIA
• For the pre-trained model conversion (related with TensorFlow) you will need TensorFlow
• For the tokenization with wordpiece model you will need WordPiece
• For the use of CRF in sequence labeling downstream task you will need pytorch-crf

Datasets

The real-world TLS 1.3 dataset is collected from March to July 2021 on China Science and Technology Network (CSTNET). For privacy considerations, we only release the anonymous data (see in CSTNET-TLS 1.3).

Other datasets we used for comparison experiments are publicly available, see the paper for more details. If you want to use your own data, please check if the data format is the same as datasets/cstnet-tls1.3/ and specify the data path in data_process/.

Using ET-BERT

You can now use ET-BERT directly through the pre-trained model or download via:

wget -O pretrained_model.bin https://drive.google.com/file/d/1r1yE34dU2W8zSqx1FkB8gCWri4DQWVtE/view?usp=sharing

After obtaining the pre-trained model, ET-BERT could be applied to the spetic task by fine-tuning at packet-level with labeled network traffic:

python3 fine-tuning/run_classifier.py --pretrained_model_path models/pre-trained_model.bin \
                                   --vocab_path models/encryptd_vocab.txt \
                                   --train_path datasets/cstnet-tls1.3/packet/train_dataset.tsv \
                                   --dev_path datasets/cstnet-tls1.3/packet/valid_dataset.tsv \
                                   --test_path datasets/cstnet-tls1.3/packet/test_dataset.tsv \
                                   --epochs_num 10 --batch_size 32 --embedding word_pos_seg \
                                   --encoder transformer --mask fully_visible \
                                   --seq_length 128 --learning_rate 2e-5

The default path of the fine-tuned classifier model is models/finetuned_model.bin. Then you can do inference with the fine-tuned model:

python3 inference/run_classifier_infer.py --load_model_path models/finetuned_model.bin \
                                          --vocab_path models/encryptd_vocab.txt \
                                          --test_path datasets/cstnet-tls1.3/packet/nolabel_test_dataset.tsv \
                                          --prediction_path datasets/cstnet-tls1.3/packet/prediction.tsv \
                                          --labels_num 120 \
                                          --embedding word_pos_seg --encoder transformer --mask fully_visible

Reproduce ET-BERT

Pre-process

To reproduce the steps necessary to pre-train ET-BERT on network traffic data, follow the following steps:

1. Run vocab_process/main.py to generate the encrypted traffic corpus or directly use the generated corpus in corpora/. Note you'll need to change the file paths and some configures at the top of the file.
2. Run main/preprocess.py to pre-process the encrypted traffic burst corpus.

      python3 preprocess.py --corpus_path corpora/encrypted_traffic_burst.txt \
                            --vocab_path models/encryptd_vocab.txt \
                            --dataset_path dataset.pt --processes_num 8 --target bert
   

3. Run data_process/main.py to generate the data for downstream tasks if there is a dataset in pcap format that needs to be processed. This process includes two steps. The first is to split pcap files by setting splitcap=True in datasets/main.py:54 and save as npy datasets. Then the second is to generate the fine-tuning data. If you use the shared datasets, then you need to create a folder under the dataset_save_path named dataset and copy the datasets here.

Pre-training

To reproduce the steps necessary to finetune ET-BERT on labeled data, run pretrain.py to pre-train. If one wishes to continue training on an already pre-trained model, the parameter --pretrained_model_path a can be increased.

   python3 pre-training/pretrain.py --dataset_path dataset.pt --vocab_path models/encryptd_vocab.txt \
                       --output_model_path models/pre-trained_model.bin \
                       --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
                       --total_steps 500000 --save_checkpoint_steps 10000 --batch_size 32 \
                       --embedding word_pos_seg --encoder transformer --mask fully_visible --target bert

Fine-tuning on downstream tasks

To see an example of how to use ET-BERT for the encrypted traffic classification tasks, go to the Using ET-BERT and run_classifier.py script in the fine-tuning folder.

Note: you'll need to change the path in programes.

Citation

If you are using the work (e.g. pre-trained model) in ET-BERT for academic work, please cite the paper published in WWW 2022:

@inproceedings{lin2022etbert,
  author    = {Xinjie Lin and
               Gang Xiong and
               Gaopeng Gou and
               Zhen Li and
               Junzheng Shi and
               Jing Yu},
  title     = {{ET-BERT:} {A} Contextualized Datagram Representation with Pre-training
               Transformers for Encrypted Traffic Classification},
  booktitle = {{WWW} '22: The {ACM} Web Conference 2022, Virtual Event, Lyon, France,
               April 25 - 29, 2022},
  pages     = {633--642},
  publisher = {{ACM}},
  year      = {2022}
}

Contact

Please post a Github issue if you have any questions.