The Problem

Identifying encrypted darknet communications (Tor, VPN traffic) poses a major cybersecurity challenge. Traditional methods struggle with sophisticated obfuscation techniques used in privacy-focused protocols.

My Solution

I built a machine learning system that detects and classifies darknet traffic with 98% accuracy for protocol detection and 93% accuracy for communication type classification using the CIC-Darknet2020 dataset.

Technical Highlights

• Smart Feature Engineering: Reduced 89 features to 10 most predictive ones while maintaining accuracy
• Handled Real-World Data: Processed 158,616 network flows with anomalies and inconsistencies
• Class Imbalance Solutions: Applied SMOTE oversampling for optimal model performance
• Dual Classification: Separate models for protocol detection and communication type identification

Impact

This system enables real-time network monitoring and helps cybersecurity professionals identify suspicious traffic patterns while maintaining efficiency for production environments.

Tech Stack: Python, scikit-learn, pandas, imbalanced-learn, NumPy

Applications: Network Security, Traffic Analysis, Cybersecurity Research