NFStream: a Flexible Network Data Analysis Framework.
Deep Learning models for network traffic classification
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
Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
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Use deep learning to classify the malicious traffic, and use TensorFlow2.0 to carry out it.
CESNET DataZoo: A toolset for large network traffic datasets
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
flowRecorder - a network traffic flow feature measurement tool
CESNET Models: Neural networks for network traffic classification
Using SIFT features, BOW, model: SVM
AutoML4ETC, a tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification.
Jupyter notebooks with traffic classification examples using CESNET DataZoo and CESNET Models packages
In this paper, we proposed a deep learning model which achieves progress compared to LeNet-5 in the stability of Internet traffic classification.
A web-based solution utilizing a robust tensorflow model for precise traffic condition classification made in ReactJs and FastAPI for backend.
A repository with models for encrypted traffic classification.
π³π‘πΆ Generate network communication data for target tasks in diverse network conditions.
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