Phishing Website Detection Using Machine Learning Algorithms

Phishing is a type of identity fraud that involves carrying sensitive information including usernames, watchwords, bank account figures, and credit card figures. People working in the field of cyber security are now looking for reliable and consistent phishing website detection solutions. The purpose of this research is to apply machine learning to detect phishing URLs by extracting and analysing different features of genuine URLs. Decision trees, KNN, logistic regression, random forest, and support vector machine algorithms are used to detect phishing websites.. The goal of the study is to find the optimal machine learning algorithm by comparing accuracy rates, false positives, and false negatives.