<p align="center">
<img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/models.svg" width="450">
</p>
[](https://github.com/CESNET/cesnet-models/blob/main/LICENCE)
[](https://cesnet.github.io/cesnet-models/)
[](https://pypi.org/project/cesnet-models/)
[](https://pypi.org/project/cesnet-models/)
The goal of this project is to provide neural network architectures for traffic classification and their pre-trained weights.
The newest network architecture is named Multi-modal CESNET v2 (mm-CESNET-v2) and is visualized in the following picture. See the [getting started](https://cesnet.github.io/cesnet-models/getting_started/) page and [models](https://cesnet.github.io/cesnet-models/reference_models/) reference for more information.
:frog: :frog: See a related project [CESNET DataZoo](https://github.com/CESNET/cesnet-datazoo) providing large TLS and QUIC traffic datasets. :frog: :frog:
:notebook: :notebook: Example Jupyter notebooks are included in a separate [CESNET Traffic Classification Examples](https://github.com/CESNET/cesnet-tcexamples) repo. :notebook: :notebook:
### Multi-modal CESNET v2
<p align="center">
<img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/model-mm-cesnet-v2.png" width="450">
</p>
## Installation
Install the package from pip with:
```bash
pip install cesnet-models
```
or for editable install with:
```bash
pip install -e git+https://github.com/CESNET/cesnet-models
```
## Papers
Models from the following papers are included:
* [Fine-grained TLS services classification with reject option](https://doi.org/10.1016/j.comnet.2022.109467) <br>
Jan Luxemburk and Tomáš Čejka <br>
Computer Networks, 2023
* [Encrypted traffic classification: the QUIC case](https://doi.org/10.23919/TMA58422.2023.10199052) <br>
Jan Luxemburk and Karel Hynek <br>
2023 7th Network Traffic Measurement and Analysis Conference (TMA)
Raw data
{
"_id": null,
"home_page": null,
"name": "cesnet-models",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "Jan Luxemburk <luxemburk@cesnet.cz>, Karel Hynek <hynekkar@cesnet.cz>",
"keywords": "traffic classification, deep learning, models",
"author": null,
"author_email": "Jan Luxemburk <luxemburk@cesnet.cz>, Karel Hynek <hynekkar@cesnet.cz>",
"download_url": "https://files.pythonhosted.org/packages/be/b2/7141d66176d2e62712ef65e9d42e9c939e934d98b4ce21be74d0336cd87f/cesnet_models-0.3.0.tar.gz",
"platform": null,
"description": "<p align=\"center\">\r\n <img src=\"https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/models.svg\" width=\"450\">\r\n</p>\r\n\r\n[](https://github.com/CESNET/cesnet-models/blob/main/LICENCE)\r\n[](https://cesnet.github.io/cesnet-models/)\r\n[](https://pypi.org/project/cesnet-models/)\r\n[](https://pypi.org/project/cesnet-models/)\r\n\r\n\r\nThe goal of this project is to provide neural network architectures for traffic classification and their pre-trained weights.\r\n\r\nThe newest network architecture is named Multi-modal CESNET v2 (mm-CESNET-v2) and is visualized in the following picture. See the [getting started](https://cesnet.github.io/cesnet-models/getting_started/) page and [models](https://cesnet.github.io/cesnet-models/reference_models/) reference for more information.\r\n\r\n:frog: :frog: See a related project [CESNET DataZoo](https://github.com/CESNET/cesnet-datazoo) providing large TLS and QUIC traffic datasets. :frog: :frog:\r\n\r\n:notebook: :notebook: Example Jupyter notebooks are included in a separate [CESNET Traffic Classification Examples](https://github.com/CESNET/cesnet-tcexamples) repo. :notebook: :notebook:\r\n\r\n### Multi-modal CESNET v2\r\n<p align=\"center\">\r\n <img src=\"https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/model-mm-cesnet-v2.png\" width=\"450\">\r\n</p>\r\n\r\n## Installation\r\n\r\nInstall the package from pip with:\r\n\r\n```bash\r\npip install cesnet-models\r\n```\r\n\r\nor for editable install with:\r\n\r\n```bash\r\npip install -e git+https://github.com/CESNET/cesnet-models\r\n```\r\n\r\n## Papers\r\n\r\nModels from the following papers are included:\r\n\r\n* [Fine-grained TLS services classification with reject option](https://doi.org/10.1016/j.comnet.2022.109467) <br>\r\nJan Luxemburk and Tom\u00e1\u0161 \u010cejka <br>\r\nComputer Networks, 2023\r\n\r\n* [Encrypted traffic classification: the QUIC case](https://doi.org/10.23919/TMA58422.2023.10199052) <br>\r\nJan Luxemburk and Karel Hynek <br>\r\n2023 7th Network Traffic Measurement and Analysis Conference (TMA)\r\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "Pre-trained neural networks for encrypted traffic classification",
"version": "0.3.0",
"project_urls": {
"Bug Tracker": "https://github.com/CESNET/cesnet-models/issues",
"Documentation": "https://cesnet.github.io/cesnet-models/",
"Homepage": "https://github.com/CESNET/cesnet-models"
},
"split_keywords": [
"traffic classification",
" deep learning",
" models"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4de0b7c0c419db72a0279e6532f2c79ae2839e0a9c1be5d3867c7cc20da1c362",
"md5": "61bcd102804d142b47c82922aa8d1ca1",
"sha256": "6d668ac13df080e9ae7612abb4a7c1a3b866990b9dae90af0b9dd50ffb5c3249"
},
"downloads": -1,
"filename": "cesnet_models-0.3.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "61bcd102804d142b47c82922aa8d1ca1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 21402,
"upload_time": "2024-10-23T08:57:33",
"upload_time_iso_8601": "2024-10-23T08:57:33.789677Z",
"url": "https://files.pythonhosted.org/packages/4d/e0/b7c0c419db72a0279e6532f2c79ae2839e0a9c1be5d3867c7cc20da1c362/cesnet_models-0.3.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "beb27141d66176d2e62712ef65e9d42e9c939e934d98b4ce21be74d0336cd87f",
"md5": "8791f27f30284564ebb1ca10e7741eb4",
"sha256": "26d9e7b2f84a05fdeffece0d0a6221d382aa28aea43b55aee9bda0e48971526d"
},
"downloads": -1,
"filename": "cesnet_models-0.3.0.tar.gz",
"has_sig": false,
"md5_digest": "8791f27f30284564ebb1ca10e7741eb4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 19641,
"upload_time": "2024-10-23T08:57:34",
"upload_time_iso_8601": "2024-10-23T08:57:34.825285Z",
"url": "https://files.pythonhosted.org/packages/be/b2/7141d66176d2e62712ef65e9d42e9c939e934d98b4ce21be74d0336cd87f/cesnet_models-0.3.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-23 08:57:34",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "CESNET",
"github_project": "cesnet-models",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "cesnet-models"
}
JSON
