Optimized Machine Learning Algorithm to classify Phishing Websites

Abstract

The common method of misleading people is Phishing, which giving up their personal information by using bogus websites. The major goal of this work is to use machine learning methods to categorize phishing websites. The internet has become a vital factor of our day-to-day life. Attackers can use smart internet-based gadgets for financial transactions as a platform to conduct a variety of assaults. The purpose of this study is to discuss phishing assaults. Phishing is a kind of friendly commercial attack that is as often as possible used to acquire individual data from users. As innovation progresses, phishing techniques have improved at a quick speed, which ought to be kept away from. As a result of the COVID-19 problems, there has been an increase in phishing assaults in which attackers have obtained users' personal information. Using phishing websites is one of the ways to begin a phishing assault. A phishing website imitates a reputable website while stealing the user's personal information. Phishing is a pernicious type of online crime that should be stayed away from assuming the public's assurance in the web is to be reestablished. Machine Learning is a viable strategy for aggressive phishing attacks. In this research, various machine learning techniques for detecting phishing websites are studied. Our suggested approach has a high degree of accuracy in detecting phishing websites.