LinkLens

Output- user ainterface

Project Story: LINKLENS

Inspiration

LINKLENS was inspired by the increasing number of phishing websites that trick users into sharing sensitive information. We wanted to develop a smart system that detects suspicious websites in real time and helps users browse more safely.

How We Built It

We developed LINKLENS using a Chrome Extension, a FastAPI backend, and a machine learning model. The system analyzes URL and behavioral features to calculate a risk score and classify websites as safe, suspicious, or high-risk. We also integrated AI-generated explanations to help users understand potential threats.

What We Learned

Through this project, we gained practical experience in machine learning, browser extension development, API integration, database management, and cybersecurity. We also learned the importance of feature engineering, testing, and handling model failures.

Challenges We Faced

Some challenges included integrating the extension with the backend, resolving machine learning model-loading issues, and improving risk assessment reliability. We also recognized the need for real-world datasets and further development of the adaptive learning mechanism.

Outcome

LINKLENS is a prototype aimed at making phishing detection more intelligent and understandable. The project strengthened our technical skills and motivated us to continue improving its accuracy, reliability, and real-time protection capabilities.