Deep Learning models for network traffic classification
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Updated
Jan 30, 2026 - Python
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
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.
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)
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
Classifying whether an encrypted HTTPS page load came from Firefox or wget using traffic shape alone — packet sizes, timings, bursts. Never payload.
A Python toolkit for identifying websites from encrypted network traffic using ML on packet metadata (no content decryption).
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
Hyperdimensional Intrusion Detection System for Zero-Day Exploit detection in encrypted traffic using Conformal Geometric Algebra, online learning, and real-time anomaly scoring.
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