AI Login Anomaly Detector

Inspiration:

We’ve all seen stories of hacked accounts and data breaches—sometimes, a single compromised login can cause massive damage. That got us thinking: What if we could detect suspicious logins before they became a problem? Inspired by fraud detection in banking and the growing need for smarter security, we built an AI-powered anomaly detector. It learns user behavior, spots unusual login attempts, and helps prevent unauthorized access. Our goal? Make online security proactive, not reactive. Because in today’s world, catching threats early makes all the difference.

What it does:

Our AI-Powered Login Anomaly Detector monitors user login activity and detects unusual patterns that could indicate unauthorized access. By analyzing factors like login time, location, device, and frequency, it identifies anomalies and flags potential security threats in real time. If a login seems suspicious—like a sudden location change or an unusual time of access—the system can trigger alerts or require additional authentication. The goal is to enhance security by proactively preventing account takeovers before they cause harm.

How we built it:

We built the AI-Powered Login Anomaly Detector using FastAPI for the backend, React for the frontend, and Auth0 for authentication. The system integrates machine learning to analyze login patterns and detect anomalies. We used FastAPI to handle authentication and API requests, while React provides a seamless user experience. Auth0 manages secure authentication, issuing tokens for protected routes. The AI model processes login data, identifying deviations from normal behavior. Throughout development, we tackled authentication challenges, API security, and model optimization to ensure accurate anomaly detection and a smooth user experience.

Challenges we ran into:

We faced several challenges while building the AI-Powered Login Anomaly Detector. Setting up Auth0 authentication was tricky, especially configuring the callback URLs, audience, and token validation. Debugging 403 Forbidden errors and resolving CORS issues took time. Integrating the machine learning model with the backend required careful handling of login data and anomaly detection thresholds. Ensuring real-time anomaly detection while keeping the system responsive was another hurdle. Despite these challenges, we pushed through, learning more about authentication, API security, and AI integration along the way.

Accomplishments that we're proud of:

We’re proud of successfully integrating AI-powered anomaly detection with a secure authentication system. Overcoming Auth0 authentication hurdles, setting up JWT validation, and ensuring smooth communication between the frontend and backend were big wins. Building a real-time detection system that flags suspicious login attempts while maintaining fast performance was another key achievement. We also enhanced our understanding of machine learning in cybersecurity and learned how to deploy a scalable security solution. Seeing our project come together and function as intended was incredibly rewarding!

What we learned

We learned how to integrate AI-driven anomaly detection with secure authentication, handle JWT validation, and troubleshoot Auth0 configurations. We deepened our understanding of FastAPI, React, and machine learning in cybersecurity while improving our problem-solving skills. Most importantly, we learned how to build a scalable and secure authentication system.

What's next for AI Login Anomaly Detector

Next, we plan to enhance the AI model's accuracy by training it on more diverse login patterns. We’ll integrate real-time alerts for detected anomalies, improve user behavior analytics, and optimize API security. Additionally, we aim to deploy it on the cloud, making it scalable for larger applications.

Built With

• docker
• fastapi
• postgresql
• python
• react
• scikit-learn
• tensorflow