<ul data-eligibleForWebStory="false"><li>An individual developed a Machine Learning system for intelligent phishing detection to protect against cybercriminals stealing personal information through phishing scams.</li><li>The system utilizes algorithms such as Logistic Regression, Random Forest, SVM, Naive Bayes, and K-Nearest Neighbors to analyze websites and differentiate between legitimate and phishing sites.</li><li>After training and testing three models on a dataset of thousands of websites, Random Forest was the most effective, and hyperparameter tuning using GridSearchCV further enhanced its accuracy.</li><li>The resulting highly accurate model with reduced false positives and false negatives is deemed successful in detecting phishing scams, and the model's output has been saved for future use.</li></ul>

Developing an Intelligent Phishing Detection System with Machine Learning

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