Cyber Threat Detection System - Project Story

About the Project The Cyber Threat Detection System is a web-based tool designed to identify and track potential cyber threats using a graph-based database. The project allows users to input threat data, query existing threats, and visualize cyber threat intelligence efficiently.

Inspiration

With the increasing number of cyber threats, detecting and mitigating them has become crucial. This project was inspired by real-world cybersecurity challenges, where threat intelligence plays a vital role in protecting systems from malicious actors.

How It Works

Users can input threat data (IP address, domain, and threat type). The backend processes the input and stores it in a database. Users can query threats based on IP addresses. The system retrieves and displays threat information to help assess risks. Technologies Used Frontend: HTML, JavaScript Backend: Flask (Python) Database: ArangoDB (Graph-based storage) API Integration: OpenAI (for threat analysis and insights) Challenges Faced Integrating ArangoDB with Python – Learning how to structure and query graph-based data efficiently. Ensuring secure data handling – Preventing SQL injections and securing API requests. Deploying the project – Managing dependencies and resolving compatibility issues in different environments. What I Learned Graph databases are powerful for cybersecurity applications. API security is crucial in handling threat intelligence. Building interactive web applications improves usability and user experience. Future Enhancements 🚀 Enhancing AI-powered threat detection using machine learning. 🔍 Improving visualization of threats with an interactive graph UI. 🔐 Strengthening data security with encryption and user authentication.

Built With

• analysis
• and
• api
• arangodb
• backend:
• database:
• flask
• for
• frontend:-html
• graph-based
• integration:
• javascript
• openai
• python)
• storage)
• threat