PhishCatch 🎣🚫 - Phishing Detection App (Final year Project)

Table of Contents 📚

• App Link 🌐
• About the App ℹ️
• Screenshots 📸
• Deployment on Heroku 🚀
• Technologies Used 🛠️
• Bug / Feature Request 🐞🔧
• Acknowledgements 🙏

App Link 🌐

To experience the power of PhishCatch, click on the link below: 👇
https://ceeriil.com/

About the App ℹ️

PhishCatch is an advanced Phishing Detection App developed as part of my final year project at the university. It is designed to protect users from phishing attempts by analyzing messages and URLs for potential threats. The app leverages machine learning techniques, including natural language processing and pattern recognition, to identify suspicious content and URLs.

With an emphasis on precision, PhishCatch employs Multinomial Naive Bayes and Bernoulli Naive Bayes classifiers to minimize false positives and ensure accurate detection. The app is built with Python 3.6.10 and utilizes popular libraries such as Scikit-Learn and NLTK for efficient analysis and prediction.

Screenshots 📸

Phishing Content Input 📝

Safe Message ✉️

Not Safe ⚠️

Phishing URL Content 🌐

Result of URL Detection ✔️

Deployment on Heroku 🚀

PhishCatch is deployed on Heroku. To access the deployed version, visit https://phishcatch.heroku.app/

Technologies Used 🛠️

Bug / Feature Request 🐞🔧

If you encounter any issues or have suggestions for improving PhishCatch, please open an issue. Your feedback is invaluable to us!

Acknowledgements 🙏

I extend my heartfelt gratitude to the open-source community and all contributors who inspired and supported the development of PhishCatch.

Please ⭐ the repository if you find PhishCatch helpful in safeguarding against phishing attacks. Together, let's create a safer digital world! 🛡️