Network Intrusion Detection System using ML
Cyber Security · Python, Scikit-learn, Wireshark, Flask
About this project
This security project captures network packets, extracts flow features and classifies traffic using trained models (Random Forest/SVM). Detected attacks — DoS, probe, R2L, U2R — are logged with severity and shown on a live dashboard with alert emails to the administrator. Includes comparison of multiple ML models with accuracy metrics for your report.
Key Features
Live packet capture and feature extraction
ML classification of attack categories
Severity-based alerting with email notifications
Model comparison with accuracy/precision/recall
Interactive detection dashboard
Project Specifications
- Domain: Cyber Security
- Technology: Python, Scikit-learn, Wireshark, Flask
- Software: Python, Scikit-learn, Wireshark/TShark, Flask
- Views: 14
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