PHISHING URL DETECTION USING GRADIENT BOOSTING CLASSIFIER

In the digital age, phishing attacks have become a prevalent threat, targeting unsuspecting users through deceptive websites designed to steal sensitive information. This project aims to develop a robust and efficient machine learning model for detecting phishing URLs, enhancing cybersecurity defenses. By leveraging a dataset of labeled URLs, this project extracts relevant features and employs a Gradient Boosting Classifier to accurately distinguish between phishing and nonphishing URLs. The methodology involves data preprocessing, feature extraction, model training, and evaluation. The trained model is deployed using a Flask web application, providing an accessible interface for real-time URL classification. The implementation demonstrates high accuracy and precision, offering a reliable tool for mitigating phishing threats and safeguarding users' online interactions. This project underscores the potential of machine learning in enhancing cybersecurity and protecting against evolving cyber threats