Detection of Phishing Websites Using Machine Learning

Abstract-Phishing is a category of cyberattack that includes the theft of credit card numbers, passwords, and other private data. We have employed machine learning algorithms to identify phishing websites in order to prevent phishing fraud. The availability of several services, including social networking, software downloads, online banking, entertainment, and education, has sped up the development of the Web in recent years. Consequently, enormous volumes of data are downloaded and uploaded to the Internet on a regular basis. Attackers can now obtain private information, including social security numbers, account numbers, passwords, and usernames, as well as financial information. This is one of the most important problems with web security and is referred to as a “phishing” attack on the internet. To identify these malicious websites, we employ a variety of machine learning methods, including KNN, Naive Bayes, Gradient Boosting, and Decision Trees. The study is broken down into the following sections. The introduction outlines the tools, methods, and concentrated zones that are employed. The process of gathering the data needed to proceed is described in depth in the preliminary section. Subsequently, the paper highlights the thorough examination of the information sources.