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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 page and models reference for more information.
:frog: :frog: See a related project 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 repo. :notebook: :notebook:
Install the package from pip with:
pip install cesnet-models
or for editable install with:
pip install -e git+https://github.com/CESNET/cesnet-models
Models from the following papers are included:
Fine-grained TLS services classification with reject option
Jan Luxemburk and Tomáš Čejka
Computer Networks, 2023
Encrypted traffic classification: the QUIC case
Jan Luxemburk and Karel Hynek
2023 7th Network Traffic Measurement and Analysis Conference (TMA)
FAQs
Pre-trained neural networks for encrypted traffic classification
We found that cesnet-models demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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