CESNET Models: Neural networks for network traffic classification
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Updated
Jun 3, 2024 - Python
CESNET Models: Neural networks for network traffic classification
Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
NFStream: a Flexible Network Data Analysis Framework.
NetFlow aggregation and graph toolkit
CESNET DataZoo: A toolset for large network traffic datasets
Jupyter notebooks with traffic classification examples using CESNET DataZoo and CESNET Models packages
pcap file analysis, only deal with ipV4
一个流量分类的封装框架
Traffic Fingerprinting using Autoencoders
Mobile Traffic Classification using Deep Learning
AutoML4ETC, a tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification.
In this paper, we proposed a deep learning model which achieves progress compared to LeNet-5 in the stability of Internet traffic classification.
Toolkit for processing PCAP file and transform into image of MNIST dataset
A web-based solution utilizing a robust tensorflow model for precise traffic condition classification made in ReactJs and FastAPI for backend.
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
This is a beginner's coursework about Net traffic classification using ML
A repository with models for encrypted traffic classification.
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
Pytorch implementation of deep packet: a novel approach for encrypted traffic classification using deep learning
Network Measurement Lab course homeworks - 2021/2022
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