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

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

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.

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

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

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

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.

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

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

Deploy encrypted payloads with polymorphic execution, cross-platform compatibility, and production-ready performance for seamless workflow integration.

Classifying whether an encrypted HTTPS page load came from Firefox or wget using traffic shape alone — packet sizes, timings, bursts. Never payload.

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

A Python toolkit for identifying websites from encrypted network traffic using ML on packet metadata (no content decryption).