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Traffic Classification Transfer Learning

This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.

This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.

  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:

    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet

  • Implements three transfer learning methods:

    • k-NN
    • Linear probing
    • Full model fine-tuning

  • Includes training from scratch and an input-space baseline for comparison.

How to Use

  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).

  2. Download all datasets

    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.

  3. Update conf/local-vars.yaml and provide:

    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)

  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.

Results

The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.

Citation

If you use this code or build upon this work, please cite:

@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}

About

This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success

Resources

Readme
BSD-3-Clause license
Activity
Custom properties

Stars

1 star

Watchers

0 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
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Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
Skip to content
BranchesTags
Repository files navigation
Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
Skip to content
BranchesTags
Repository files navigation
Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
Skip to content
BranchesTags
Repository files navigation
Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
Skip to content
BranchesTags
Repository files navigation
Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
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GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
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Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - CESNET/tc-transfer: This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success · GitHub
Skip to content
BranchesTags
Repository files navigation
Traffic Classification Transfer Learning
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success, https://doi.org/10.48550/arXiv.2502.12930.
This project:

  • Loads the pretrained model 30pktTCNET_256 from CESNET Models, with the pretraining procedure detailed in the publication.
  • Transfers the model to seven well-known traffic classification datasets (ten downstream tasks in total) to evaluate how well it generalizes:
    • ISCXVPN2016
    • MIRAGE19
    • MIRAGE22
    • UTMOBILENET21
    • UCDAVIS19
    • CESNET-TLS22
    • AppClassNet
  • Implements three transfer learning methods:
    • k-NN
    • Linear probing
    • Full model fine-tuning
  • Includes training from scratch and an input-space baseline for comparison.
How to Use
  1. Install the dependencies listed in ./requirements/pip-requirements.txt. If you are using Windows, you can create the Conda environment from ./requirements/environment-windows.yml. Ensure that PyTorch is installed with CUDA support. Install faiss either as faiss-cpu or faiss-gpu (pip packages are also available).
  2. Download all datasets
    • MIRAGE19, MIRAGE22, UTMOBILENET21, and UCDAVIS19 are obtained from the tcbench framework. Follow the tcbench instructions to install the datasets.
    • CESNET-TLS22 is accessed via CESNET DataZoo. It is downloaded automatically on first use.
    • Download AppClassNet from figshare.
    • For ISCXVPN2016, we used a version provided by Alfredo Nascita. You can contact him at alfredo[dot]nascita[at]unina[dot]it. Before use, process the dataset with scripts/preprocess_iscx_dataset.py.
  3. Update conf/local-vars.yaml and provide:
    • A local folder containing the AppClassNet and ISCXVPN2016 datasets
    • A temporary directory for experiment outputs
    • A wandb project name (wandb integration for experiment tracking is currently mandatory)
  4. Experiments are configured with Hydra, with configuration files located in ./conf. To run an experiment with a specific configuration, use python -m experiment_wrapper.do_experiment --config-name local-config.yaml.
Results
The results of the experiments are saved in $temp_dir/results. The final results presented in the publication are available in scripts/final-results. For an overview of the results, see scripts/explore_results.ipynb. The best model fine-tuning hyperparameters for each dataset can be found in conf/best.
Citation
If you use this code or build upon this work, please cite:
@article{Luxemburk2026Universal,
author={Luxemburk, Jan and Hynek, Karel and Plný, Richard and Čejka, Tomáš},
journal={IEEE Transactions on Network and Service Management},
title={Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success},
year={2026},
volume={23},
pages={1647-1663},
doi={10.1109/TNSM.2025.3642984}
}
About
This project provides code to reproduce the transfer learning experiments from Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages