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phishing-sites

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The primary objective of this project is to equip users with a powerful Machine Learning-driven application to proactively defend against phishing threats and identify malicious URLs. By leveraging a range of ML algorithms, including decision trees, Random Forests, MLP, SVM, XGBoost algorithm, and more, our robust model ensures accurate detection.

  • Updated Jan 11, 2024
  • Jupyter Notebook
Malicious-URLs-DB
Watchlist-Bot

Bypass 2-factor authentication. Advance Phishing framework that can use your own browser as machine-in-the-middle to receive any input from the victim through a mimic website. Test it in your own lab. Author is not responsible for any misuse. Developed by Nuhu Tahiru

  • Updated Mar 23, 2024
  • PHP

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