NIDS - Network Intrusion Detection System

Overview

NIDS (Network Intrusion Detection System) is a Machine Learning based web application designed to detect and classify malicious network traffic using the NSL-KDD dataset.

Checkout-https://nids-qf5t.onrender.com

The system analyzes incoming network traffic features and predicts whether the traffic is:

• Normal
• DOS Attack
• PROBE Attack
• R2L Attack
• U2R Attack

This project combines Machine Learning and Cybersecurity concepts to provide a real-time intrusion detection solution through an interactive web interface.

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Features

• Machine Learning based intrusion detection
• Real-time attack prediction
• Multiple attack category classification
• Flask powered web application
• User-friendly frontend interface
• NSL-KDD dataset integration
• Responsive UI design
• Deployable on cloud platforms like Render

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Tech Stack

Frontend

• HTML5
• CSS3

Backend

• Flask

Machine Learning

• Scikit-learn
• Pandas
• NumPy
• Joblib

Deployment

• GitHub
• Render

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Project Structure

NIDS/
│
├── app.py
├── train_model.py
├── requirements.txt
├── Procfile
├── runtime.txt
├── model.pkl
├── label_encoder.pkl
├── protocol_encoder.pkl
├── service_encoder.pkl
├── flag_encoder.pkl
│
├── data/
│   ├── Train.txt
│   └── Test.txt
│
├── static/
│   └── style.css
│
├── templates/
│   ├── index.html
│   └── prediction.html
│
└── README.md

LICENSE

This project is licensed under the MIT License.