Robust hacker-influenced interface developed with Streamlit.
Passing trusted websites such as "github.com" through the checker will flag it as low risk as expected.
Passing malicious websites such as "gi1hub.com/trojan.git" flags them as high risk and explains why, helping users stay informed.

Phish-Net: Smarter Phishing Detection

Inspiration

Phishing is a widespread cybersecurity threat, and almost everyone has encountered a phishing attempt at some point. We wanted to create an application that empowers users to detect and avoid these malicious attacks.

What It Does

Phish-Net is a powerful phishing detection tool designed to analyze URLs and text, helping users identify potential phishing attempts with ease.

How We Built It

We developed Phish-Net with a hacker-themed interface to provide a sleek and intuitive user experience. The application leverages AI to analyze threats using a predefined prompt, ensuring accurate and informative feedback on potential phishing risks.

Challenges We Faced

Initially, we struggled to get Streamlit running, but after troubleshooting, we managed to resolve the issue.
Designing an organized and visually appealing webpage also proved challenging, but we refined it through iteration.

Accomplishments We're Proud Of

Learning new technologies – We explored Gemini and Streamlit to enhance our project.
Expanding our coding skills – We became more comfortable working with PYTHON and CSS, especially with not having experience building a project with both complementing each other.

What We Learned

We gained hands-on experience integrating external software to create a functional and impactful project.

What's Next for Phish-Net

We plan to introduce user reporting features, allowing users to contribute data that helps improve the algorithm’s phishing detection capabilities.