Network Intrusion Detection & Threat Classification System Using Machine Learning
Real-time network traffic packet inspection and anomaly classifier trained on NSL-KDD and CICIDS2017 datasets.
Problem Statement
Traditional signature-based Firewalls cannot detect novel Zero-Day exploits, DoS/DDoS variants, port sweeps, and botnet infiltration in modern enterprise networks.
Proposed Solution
An AI-powered Network Intrusion Detection System (NIDS) that sniffs live packets via Scapy, extracts 41 behavioral statistical features, and classifies attacks using an ensemble XGBoost and Random Forest classifier with 98.4% detection accuracy.
Key System Features
System Architecture & Pipeline
End-to-end data transformation pipeline from input capture to output visualization.
Project Modules Breakdown
Packet Ingestion & Feature Engineering
Module 1Uses Scapy to sniff raw NIC sockets and compute flow durations, packet counts, and byte rates.
Ensemble ML Detection Engine
Module 2Pre-trained multi-class XGBoost model classifying normal traffic vs malicious attack vectors.
SOC Security Web Dashboard
Module 3Live web monitoring console showing network traffic velocity, active threats, and flagged host IPs.
Complete Technology Stack
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