Pre-trained neural networks for encrypted traffic classification
Project description
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:
Multi-modal CESNET v2
Installation
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
Papers
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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for cesnet_models-0.2.6-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b33a22da946e02926d432ac540cf50a71cb2b11ef183bde31562903f3b07c5ad |
|
MD5 | a70ef2f29b8e1c4b66f7d3fe6d02ff88 |
|
BLAKE2b-256 | 304b96fecb72232e941c9ddbf8086f7e2cd3e5a15f26dbc56653c12e0d4d403c |