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phishing-attacks

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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

Advancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.

  • Updated Jan 12, 2024
  • Jupyter Notebook

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