🔐 Network Security System

An end-to-end Machine Learning project for detecting malicious / phishing URLs and strengthening network security.
This project covers the full ML lifecycle — data ingestion, preprocessing, model training, experiment tracking with MLflow, deployment using FastAPI + Uvicorn, and containerization with Docker.

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🚀 Features

• ✅ Data ingestion & preprocessing pipeline for URL/network data
• ✅ ML model training with Scikit-learn & experiment tracking via MLflow
• ✅ End-to-end pipeline orchestration
• ✅ REST API deployment using FastAPI + Uvicorn
• ✅ Docker support for containerized deployment
• ✅ Interactive Swagger UI at /docs

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📂 Project Structure

networksecurity/
│── networksecurity/        # Core Python package (pipelines, utils, trainer, etc.)
│── templates/              # FastAPI Jinja2 templates
│── Artifacts/              # Generated pipeline artifacts (ignored in .gitignore)
│── final\_model/            # Trained model + preprocessor (ignored in .gitignore)
│── mlruns/                 # MLflow experiment logs
│── valid\_data/             # Validation dataset
│── app.py                  # FastAPI app
│── main.py                 # API entrypoint
│── push\_data.py            # Script to push sample data into the pipeline
│── requirements.txt        # Project dependencies
│── Dockerfile              # Containerization setup
│── setup.py                # Package setup file

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⚡ Quickstart

1️⃣ Clone the repository

git clone https://github.com/Tridib2510/networksecurity.git
cd networksecurity

2️⃣ Create virtual environment & install dependencies

python -m venv venv
source venv/bin/activate   # (Linux/Mac)
venv\Scripts\activate      # (Windows)

pip install --upgrade pip
pip install -r requirements.txt

3️⃣ Run the API

uvicorn main:app --reload

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://127.0.0.1:8000/redoc

4️⃣ Run with Docker (optional)

docker build -t networksecurity .
docker run -p 8000:8000 networksecurity

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📊 MLflow Tracking

• All experiment runs and metrics are logged under mlruns/.
• You can start the MLflow UI with:

mlflow ui

Open http://127.0.0.1:5000 to view experiments.

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🛠 Tech Stack

• Python 3.11
• FastAPI + Uvicorn
• Scikit-learn
• Pandas / NumPy
• MLflow
• Docker

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🧪 Example Usage

import requests

url = "http://127.0.0.1:8000/predict"
data = {"url": "http://phishing-example.com"}

response = requests.post(url, json=data)
print(response.json())

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📜 License

This project is licensed under the MIT License. Feel free to use, modify, and distribute with attribution.

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👨‍💻 Author

Tridib Roy Chowdhury 🔗 LinkedIn | GitHub

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👉 This README is structured, professional, and recruiter/colleague-friendly.

Would you like me to also make a badges section (Python, FastAPI, Docker, MLflow, etc.) at the top of the README so it looks more polished on GitHub?