Intrusion Detection System
Project Architecture DiagramAWS
Terraform
Docker
Flask
NextJS
Project Overview
This system classifies network traffic as "normal" or "malicious" using a machine learning model trained on the NSL-KDD dataset. The application, built with Flask, allows users to upload data and visualize results.
Terraform is used to provision AWS resources, including ECS Fargate for containerized deployment. The CI/CD pipeline, implemented with GitHub Actions, automates testing, image scanning, and deployment across Dev, Staging, and Production environments.
Terraform is used to provision AWS resources, including ECS Fargate for containerized deployment. The CI/CD pipeline, implemented with GitHub Actions, automates testing, image scanning, and deployment across Dev, Staging, and Production environments.
Key Features
- User Authentication: Secure login and personalized storage of classification results.
- Data Classification: Process and classify NSL-KDD formatted network traffic data with a machine learning model.
- Visualization: Dynamic charts displaying insights like malicious traffic percentages.
- Scalability & Security:Scalability & Security: Deployed on AWS, leveraging Docker, Terraform, and AWS ECS Fargate for a fault-tolerant and secure architecture.
Tech Stack
- Frontend: Next.js with Tailwind CSS.
- Backend: Flask with JWT-based authentication
- Machine Learning: scikit-learn with joblib for model deployment.
- Infrastructure:Infrastructure: Docker, Terraform, AWS (CodePipeline, ECS, ECR).
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How it works
- Users upload network data in NSL-KDD format.
- Data is validated, preprocessed, and analyzed by a trained machine learning model.
- Results are visualized as interactive charts and displayed in the app.