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

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)

一个流量分类的封装框架

CESNET DataZoo: A toolset for large network traffic datasets

CESNET Models: Neural networks for network traffic classification

flowRecorder - a network traffic flow feature measurement tool

Using SIFT features, BOW, model: SVM

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.

🐳📡🐶 Generate network communication data for target tasks in diverse network conditions.

This repository contains code of the paper "Gotta Detect ’Em All: Fake Base Station and Multi-Step Attack Detection in Cellular Networks" for detecting Fake Base Stations (FBS) and Multi-Step Attacks (MSAs) from cellular network traces in the User Equipment (UE).

Traffic Fingerprinting using Autoencoders

This is a beginner's coursework about Net traffic classification using ML

Analyzing and comparing encrypted network traffic from popular applications to identify patterns and application fingerprints.

A network sniffer application that captures and analyzes network traffic using machine learning to detect malicious activity. Integrated with Kafka for real-time event streaming and Flask for a web interface that provides real-time alerts. Fully Dockerized for easy deployment.