Honeypots are powerful tools for detecting and analyzing malicious activities, but their setup can often be complex and time-consuming. I wanted to simplify this process, making honeypots more accessible and usable, even for those with minimal technical expertise.
What I Learned Through this project, I learned a great deal about:
Infrastructure Automation: I gained experience using Terraform to dynamically provision honeypots and automate resource management. Web Development: Building a React-based frontend allowed me to create an intuitive and interactive user interface. Backend Development: Using Flask, I developed robust API endpoints that connect the frontend with the backend and integrate with cloud services. Cloud Services: I explored AWS services such as EC2 and S3 for hosting honeypots and managing logs. Cybersecurity Practices: Configuring and deploying a Cowrie honeypot helped me better understand how attackers operate and how logs can provide valuable insights into malicious behaviors. How I Built It Frontend: I used React and TypeScript to design a user-friendly interface that enables easy deployment and management of honeypots. The frontend provides functionality to add honeypots, view logs, and monitor activity in real-time. Backend: The Flask backend handles API requests, facilitates deployment automation with Terraform, and integrates with AWS S3 for log storage and retrieval. Automation: I wrote Terraform scripts to provision honeypots on AWS with scalability and security in mind. Containerization: By using Docker and Docker Compose, I containerized the frontend and backend, ensuring consistency across different environments and simplifying deployment. Challenges I Faced Terraform Integration: Automating the deployment of honeypots required careful handling of configurations and AWS resource management. Real-Time Logging: Fetching logs dynamically from AWS S3 and displaying them in the frontend was a technical challenge that required careful optimization. Docker Networking: Setting up seamless communication between frontend and backend containers in a Docker Compose environment took some troubleshooting. Error Handling: Managing errors during deployment, such as AWS resource constraints or bucket access issues, and ensuring the user receives clear feedback, was another hurdle.
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