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A network intrusion detection system that analyses traffic features and classifies connections as normal or as specific attack types using machine learning.
Problem Statement
Signature-based detection cannot catch new attack patterns, and security teams are overwhelmed by alerts.
Objectives
Extract features from network traffic
Train models to classify attack types
Detect anomalies in live traffic
Display alerts on a monitoring dashboard
Modules
Traffic Capture
Packet capture and flow feature extraction.
Detection Models
Supervised classifiers and an anomaly detector.
Alert Engine
Severity scoring and alert deduplication.
Dashboard
Live traffic view, alerts, and model metrics.
Expected Output
A system that monitors network traffic and raises labelled alerts such as port scan or denial of service on a dashboard.
Future Scope
Deep learning on raw packets, automatic blocking rules, and integration with SIEM tools.