Network Security System with MLOps Pipeline

A comprehensive end-to-end MLOps project that implements a Network Security System using machine learning for threat detection and analysis. This project demonstrates professional-grade software engineering practices, MLOps principles, and cloud integration.

🚀 Features

• End-to-End MLOps Pipeline: Complete workflow from data ingestion to model deployment
• Automated ML Pipeline: Automated data validation, transformation, and model training
• Cloud Integration: Ready for cloud deployment with infrastructure-as-code support
• MLflow Integration: Model tracking and experiment management
• RESTful API: FastAPI-based API for model inference and training
• Modular Architecture: Well-structured, maintainable codebase following best practices

🛠️ Technologies Used

• Python 3.x
• FastAPI
• Scikit-learn
• MLflow
• DAGsHub
• AWS
• Docker
• Git

📊 Project Dashboard

CI/CD Pipeline

Successful deployment pipeline with continuous integration, delivery, and deployment stages

Model Performance Tracking

Model tracking in MLflow showing excellent performance metrics (F1: 0.968, Precision: 0.965, Recall: 0.972)

GitHub Integration

Project repository with MLOps experiment tracking

Cloud Deployment

Docker images stored in Amazon Elastic Container Registry

Artifact and model storage in Amazon S3

📁 Project Structure

networksecurity/
├── cloud/           # Cloud infrastructure and deployment configs
├── components/      # Core ML pipeline components
├── constant/        # Configuration constants
├── entity/         # Data models and entities
├── exception/      # Custom exception handling
├── logging/        # Logging configuration
├── pipeline/       # ETL and ML workflows
└── utils/          # Utility functions and helpers

🏗️ Architecture

The project follows a modular architecture with clear separation of concerns:

• Data Ingestion: Handles data collection and preprocessing
• Data Validation: Ensures data quality and consistency
• Data Transformation: Feature engineering and data preparation
• Model Training: Automated model training with hyperparameter tuning
• Model Evaluation: Comprehensive model evaluation metrics
• API Layer: RESTful endpoints for model inference

🚀 Getting Started

Local Setup

1. Clone the repository:

git clone https://github.com/yourusername/NetworkSecuritySystem.git
cd NetworkSecuritySystem

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

4. Run the application:

uvicorn app:app --reload

AWS EC2 Deployment

1. Connect to your EC2 instance using SSH

2. Update the system packages:

sudo apt-get update -y
sudo apt-get upgrade -y

3. Install Docker:

curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

4. Add your user to the Docker group:

sudo usermod -aG docker ubuntu
newgrp docker

5. Clone and set up the project:

git clone https://github.com/yourusername/NetworkSecuritySystem.git
cd NetworkSecuritySystem

6. Build and run with Docker:

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

📊 API Endpoints

• GET /: Health check endpoint
• POST /train: Trigger model training pipeline
• POST /predict: Get predictions from the trained model

📈 Model Performance

Our model achieves excellent performance metrics:

• F1 Score: 0.968
• Precision: 0.965
• Recall: 0.972

🔒 Security Features

• Network traffic analysis
• Anomaly detection
• Threat classification
• Real-time monitoring capabilities

👤 Author

Anusha Chebolu

• Email: [email protected]
• LinkedIn: https://www.linkedin.com/in/chebolu-anusha/