encrypted-traffic

Deep Learning models for network traffic classification

A New version of Python3 botnet, old version: http://github.com/Leeon123/Python3-botnet

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

CESNET DataZoo: A toolset for large network traffic datasets

CESNET Models: Neural networks for network traffic classification

Hotspot Shield VPN Pro 2026 for Windows — the complete professional build with every module enabled: secure tunnels, encrypted traffic, and privacy protection, no edition limits after install.

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

Jupyter notebooks with traffic classification examples using CESNET DataZoo and CESNET Models packages

TunnelBear VPN Max for Windows — the complete professional build with every module enabled: secure tunnels, encrypted traffic, and privacy protection, no edition limits after install.

Harness for evaluating encrypted traffic classification under group-aware and temporal splits. Finds that packet size sequences carry 98.5% of the signal, and documents a label-encoding bug that silently degrades results by two thirds.

PCAP → ML tensor extraction for network intrusion detection research.

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

Reproducibility code for behavioral information leakage analysis across Tor, I2P, FreeNet, and ZeroNet.

Zero-Shot Malware Traffic Recognition Across Encrypted Protocols using Seq2Vec and Ensemble CNN-GRU

Fine-Grained Encrypted Traffic Classification using Graph Neural Networks (GraphSAGE & GAT)

Implementation of the Fingerprint Complexity Score for encrypted darknet service analysis across anonymity networks.

Iterative leak-surface peeling — a falsifiability-anchored methodology for ML detection of blockchain validator infrastructure attacks. Working draft; v1 ships when arXiv submission lands.

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

Passive Measurement of QUIC and ECH at the ISP Edge

面向流量分析与分类研究的 PCAP 数据集采集工具，支持授权环境下的加密代理隧道流量和普通网站流量采集