Inbriq

Inspiration

We have studied cybersecurity in my college cousre and always had this idea to create a system where the ai agent will act like a firewall and block incomming attacks making the networks more safe and less vulnerable

What it does

Inbriq is a Self-adaptive cybersecurity platform that leverages multi-agent AI technologies to provide real-time threat detection, automated response, and continuous learning capabilities. Built for the modern cybersecurity landscape with quantum-resistant cryptography and federated learning.

How we built it

We built Inbriq as an adaptive, multi-layered security platform by leveraging modern, asynchronous architecture. The core uses Python and FastAPI to ensure high-performance, achieving sub-200ms threat detection and response latency. We moved beyond static defenses by implementing a Multi-Agent AI system where specialized agents coordinate behavioral analysis and threat identification using libraries like Scikit-learn. To ensure continuous improvement and privacy, we integrated Federated Learning for collaborative intelligence. We future-proofed the platform by building in quantum-resistant cryptography and a verifiable Blockchain Audit Trail. Finally, the user experience features a dynamic dashboard with Three.js for real-time 3D network visualization, making complex threat data intuitive. This combination of speed, intelligence, and future security defines how Inbriq was built.

Challenges we ran into

The main challenges we got stuck was how to orchestrate the multiagent system perfectly and how to make the system quantum resistant

Accomplishments that we're proud of

We are proud that our project being still in the prototype phase has done Detection Accuracy: 95%+ for known threats Response Time: <200ms average decision latency False Positive Rate: <2% with continuous learning Throughput: 1000+ packets/second analysis on the private datasets.

What's next for Inbriq

In future we want to make this system much more better and resistant against various types of new attack. We also aim to make this as a integrated system for each and every devices so that every network can become more secure.

Built With

• asyncio
• fastapi
• html5/css3
• javascript
• joblib
• numpy
• pandas
• python
• scikit-learn
• three.js
• websockets