🌐 Network Security Project for Phishing Data

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🚀 Project Overview

The Network Security Project is a comprehensive solution designed to detect phishing activities using advanced machine learning techniques. The project seamlessly integrates data ingestion, preprocessing, model training, and prediction into an interactive and scalable pipeline.

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✨ Features

• 🔍 Phishing Detection: Leverage trained models to detect phishing activities.
• 🛠️ Interactive API: FastAPI-powered endpoints for training and predictions.
• 💾 MongoDB Integration: Efficiently manage data ingestion.
• 📊 Model Tracking: Track experiments and results with MLflow.
• 🐳 Dockerized Deployment: Simplified application setup with AWS ECR(Elastic Container Registry).
• ✅ Data Validation: Schema-driven data validation for accuracy.

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🛠️ Installation Guide

1. Clone the repository:

   git clone https://github.com/ArpitKadam/Network_Security.git
   cd Network_Security

2. Set up a virtual environment:

   python3 -m venv myenv
   myenv\Scripts\activate  # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables: Create a .env file and add your MongoDB connection string:

   MONGO_DB_URL=mongodb+srv://<username>:<password>@cluster.mongodb.net/<database>

5. Run the application:

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

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💡 Technologies Used

• Programming: Python
• Frameworks: FastAPI, scikit-learn
• Database: MongoDB
• Deployment: Docker
• Experiment Tracking: MLflow, Dagshub

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🌟 DAGsHub Integration

Enhance your workflow with DAGsHub:

1. Log in to DAGsHub.
2. Connect your GitHub repository.
3. Configure MLflow tracking:

   mlflow.set_tracking_uri("https://dagshub.com/<username>/<repo>.mlflow")

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🔍 MLflow Tracking

Track experiments and monitor models:

1. Start the MLflow server:

   mlflow ui

2. Access the dashboard at http://127.0.0.1:5000.

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🌐 MongoDB Linking Guide

1. Create a MongoDB Atlas cluster.
2. Obtain your connection string:

   mongodb+srv://<username>:<password>@cluster.mongodb.net/<database>
   

3. Add the connection string to .env:

   MONGO_DB_URL=<your_connection_string>

4. Verify the integration with this snippet:

   from pymongo import MongoClient
   import certifi
   
   ca = certifi.where()
   client = MongoClient(mongo_db_url, tlsCAFile=ca)
   database = client["<database_name>"]
   collection = database["<collection_name>"]

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📂 Project Structure

├── Artifacts              # Generated files and models
├── data_schema            # Data validation schemas
├── final_models           # Pretrained models
├── logs                   # Logs for execution
├── mlruns                 # MLflow experiment data
├── networksecurity        # Source code modules
├── prediction_output      # Prediction results
├── templates              # HTML templates
├── Dockerfile             # Docker configuration
├── requirements.txt       # Dependencies
├── app.py                 # Main application
└── setup.py               # Project setup

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🤝 Contributing

We welcome contributions! Fork the repository, make your changes, and submit a pull request.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🌟 Get Started Today!

Ready to explore the world of phishing detection? Follow the steps above and bring this project to life!