Deep Learning models for network traffic classification
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Updated
Dec 26, 2021 - Python
Deep Learning models for network traffic classification
NFStream: a Flexible Network Data Analysis Framework.
Toolkit for processing PCAP file and transform into image of MNIST dataset
Pytorch implementation of deep packet: a novel approach for encrypted traffic classification using deep learning
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
flowRecorder - a network traffic flow feature measurement tool
一个流量分类的封装框架
Use deep learning to classify the malicious traffic, and use TensorFlow2.0 to carry out it.
Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
Using SIFT features, BOW, model: SVM
🐳📡🐶 Generate network communication data for target tasks in diverse network conditions.
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
CESNET Models: Neural networks for network traffic classification
NetFlow aggregation and graph toolkit
CESNET DataZoo: A toolset for large network traffic datasets
Traffic Fingerprinting using Autoencoders
pcap file analysis, only deal with ipV4
This is a beginner's coursework about Net traffic classification using ML
A repository with models for encrypted traffic classification.
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