Final Project Solution

Automated Phishing Detection Using Machine Learning Techniques

Enter a URL and the machine learning model will classify it as Safe or Phishing with a confidence score and risk explanation.

Random Forest URL Feature Extraction Real-time Prediction
Detection TypeURL Based
BackendFlask
ModelMachine Learning
HostingcPanel Ready

Check a URL

Example: https://google.com or http://paypal-login-security.xyz

🔗

Recent Detection History

Safe

bit.ly/4uCpllo

2026-05-15 05:37:17
65.0%
Safe

ln.run/MMr0t

2026-05-15 05:36:30
65.91%
Safe

https://studentportal.diu.edu.bd/

2026-05-14 06:04:36
63.64%
Phishing

http://paypal-login-security.xyz

2026-05-14 06:03:47
100.0%
Safe

https://apd.levvn.com/api/check?url=http://verify-bank-account-update-login.ru

2026-05-14 00:51:45
58.64%
Safe

facebook.com

2026-05-14 00:17:55
68.18%
Safe

https://mail.google.com/mail/u/0/#spam

2026-05-14 00:17:47
95.0%
Safe

https://mail.google.com/mail/u/0/#spam

2026-05-14 00:17:39
95.0%
1. Feature Extraction

URL length, HTTPS, IP address, suspicious words, dots, hyphens and symbols are converted into numeric features.

2. ML Classification

A trained Random Forest model learns patterns from safe and phishing examples and predicts unknown URLs.

3. Automated Result

The app instantly shows Safe or Phishing with confidence score and explanation.