The Utilization of a Unique Machine Learning Technique for the Detection of Phishing Websites

Phishing attacks are dangerous because they trick users into giving over private information. The usefulness of machine learning in identifying phishing URLs is examined in this study. We use a large dataset that includes URL structure, website content, and external data to train and assess several models. Key features are found through exploratory data analysis, and feature engineering improves the model's capabilities even more. Analysis shows that the model's decisions are greatly influenced by the existence of HTTPS, URL anchor text, and website traffic patterns. We stress the value of user education as an additional defensive mechanism and recognise the necessity of frequent model changes as a result of changing phishing techniques. Future research directions include looking into ensemble models, examining external data sources, and keeping an eye on phishing trends in order to develop better detection techniques. Keywords Phishing Detection, Machine Learning, Gradient Boosting, Feature Importance