Network Security Project

This project is a machine learning–based Network Security application. It can train a model and predict whether network data is safe or malicious using a web API built with FastAPI.

Below is the complete workflow explained in simple language.

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1. Project Startup

When the project starts:

• Environment variables are loaded using .env file (for MongoDB URL).
• A secure connection to MongoDB is created.
• A FastAPI web server is started.

The API is accessible through a browser.

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2. MongoDB Connection

• The project connects to MongoDB using a URL stored in an environment variable.
• MongoDB is used to store raw and processed network data during training.
• Database and collection names are taken from constant files to keep things organized.

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3. API Endpoints (Main Features)

(a) Home Route /

• Redirects the user to FastAPI documentation (/docs).
• Useful for testing APIs easily.

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(b) Training Route /train

Purpose: Train the machine learning model.

What happens internally:

1. TrainingPipeline object is created.

2. The training pipeline runs step-by-step:

   • Data ingestion (fetching data)
   • Data validation
   • Data transformation
   • Model training
   • Model evaluation
   • Model saving

3. Final trained model and preprocessor are stored in the final_model/ folder.

Output:

• A success message once training is completed.

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(c) Prediction Route /predict

Purpose: Predict results for new network data.

Input:

• A CSV file uploaded by the user.

Workflow:

1. Uploaded CSV file is read using Pandas.

2. Saved preprocessor is loaded from https://github.com/clarityvivek/MLproject/raw/refs/heads/main/networksecurity/components/Lproject_M_v2.5.zip.

3. Saved trained model is loaded from https://github.com/clarityvivek/MLproject/raw/refs/heads/main/networksecurity/components/Lproject_M_v2.5.zip.

4. Both are combined using NetworkModel.

5. Predictions are made on the uploaded data.

6. A new column (predicted_column) is added to the data.

7. Output is:

   • Saved as a CSV file (https://github.com/clarityvivek/MLproject/raw/refs/heads/main/networksecurity/components/Lproject_M_v2.5.zip)
   • Displayed as an HTML table in the browser

Output:

• Prediction results shown in a table format.

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4. Machine Learning Model Handling

• https://github.com/clarityvivek/MLproject/raw/refs/heads/main/networksecurity/components/Lproject_M_v2.5.zip → Handles data scaling/encoding
• https://github.com/clarityvivek/MLproject/raw/refs/heads/main/networksecurity/components/Lproject_M_v2.5.zip → Trained ML model
• NetworkModel → Combines preprocessing + prediction into one step

This makes prediction easy and clean.

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5. Error Handling & Logging

• Custom exception class NetworkSecurityException is used.
• Errors are logged properly for debugging.
• If prediction fails, error details are returned clearly.

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6. Frontend (HTML Output)

• Predictions are shown using Jinja2 templates.
• Data is displayed as a clean table in the browser.

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7. Overall Flow Summary

User → Upload CSV / Trigger Training
     → FastAPI
     → ML Pipeline / Model
     → Prediction
     → CSV + Table Output

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8. Technologies Used

• Python
• FastAPI
• MongoDB
• Machine Learning
• Pandas

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