Comparing Different Machine Learning Techniques for Detecting Phishing Websites

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Phishing site URLs are designed to gather confidential data such as user identities, passwords, and transactions involving online money. Phishing strategies have begun to advance quickly as technology advances; this could be avoided by using anti-phishing tools to identify phishing. Employing machine learning techniques to identify fraudulent websites was previously suggested and put into practice. This project's primary goal is to develop the system in a way that is highly efficient, accurate, and economical. Delivered to the system, the dataset of genuine and phishing URLs is pre-processed to put the data in a format that can be used for analysis. Each category has unique, defined phishing features against a dataset of real and fake URLs. We evaluated the classifier's performance using a different test set after training it and its values. A classifier has been created for phishing websites and tested for effectiveness with a set of labeled phishing and legal URLs. When compared to seven different classifiers of machine learning, the proposed model scored the greatest test accuracy of up to 97.5% with the Gradient Boosting Classifier.