🛡️ Network Security System

📌 Overview

The Network Security System is an end-to-end phishing detection pipeline built with FastAPI and modular ML components.
It allows users to either:

1. Upload a CSV file for batch phishing detection (results shown in an HTML table).
2. Use the REST API endpoints programmatically for training and prediction.

The system demonstrates production-style ML engineering practices, including modular pipelines, centralized logging, Docker-based deployment, and integration with cloud platforms (Azure).

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🚀 Live Demo

🌐 Web App: https://networksecurity-h4hsc3bph3fmevfq.centralus-01.azurewebsites.net
📦 Docker Hub: https://hub.docker.com/r/raw9k/network-security-app

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⚙️ Features

• Training Pipeline: Retrain the phishing detection model via API (/train).
• Batch Prediction: Upload CSVs for phishing detection; results saved as CSV and rendered as HTML tables.
• Dual Usage: Accessible both through a browser-based interface and API clients (e.g., Postman, curl).
• Optional Database Support: MongoDB integration for ingestion (can be disabled in deployment).
• Cloud-Ready Deployment: Containerized with Docker, deployed on Azure Web App for Containers.

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🧑‍💻 Usage

1. Web Interface

• Go to the deployed Azure app.
• Upload a CSV file containing URL features.
• Receive phishing detection results in a styled HTML table with summary statistics.

2. API Endpoints:

• GET / → Redirects to API docs.
• POST /predict → Upload a CSV file for predictions (returns results as JSON or HTML).
• GET /train → Retrains the ML pipeline (requires MongoDB).

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🛠️ Tech Stack

• Language: Python 3.10+
• Backend: FastAPI, Starlette, Uvicorn
• Machine Learning: Scikit-learn, Pandas, Numpy
• Deployment: Docker, Azure Web App
• Optional Storage: MongoDB (for ingestion + retraining)
• Templating: Jinja2 + Bootstrap (UI for CSV upload & results)

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Docker (optional, for containerized deployment)
• MongoDB instance (only required if you want retraining from raw data ingestion)

Installation

git clone https://github.com/raw9k/network-security-system.git
cd network-security-system
pip install -r requirements.txt

Run Locally

uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Now you can access the system:

API Docs → http://127.0.0.1:8000/docs

Home Page → http://127.0.0.1:8000/

🐳 Docker Deployment

Build and run the Docker container:

docker build -t network-security-system .
docker run -p 8000:8000 network-security-system

📊 Example Workflow

1. Open the home page in browser: http://localhost:8000
2. Upload a CSV file of URL features for batch analysis.
3. View phishing predictions in a styled HTML table.
4. Or, go to /docs for Swagger UI to interact with the API.

☁️ Deployment on Azure

1. Built into a Docker image and deployed to Azure Web App for Containers.
2. Supports both batch file uploads and REST API usage.
3. CI/CD integration possible via GitHub Actions.

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📝 Conclusion

The Network Security System combines machine learning, REST APIs, and cloud deployment into a single project. With its modular design, Docker support, and Azure deployment, it serves as a strong prototype for real-world phishing detection systems, suitable for browser plugins, secure email gateways, and enterprise security monitoring.