Phishing Detection Using Machine Learning Algorithms

Abstract

Phishing attacks are one of the most serious hazards to individuals and businesses in today's digital world. Many attacks are performed each month with the goal of convincing consumers that they are visiting a reputable website or online application in order to obtain account information. A strategy is provided for detecting these types of assaults by modifying existing Document Object Model (DOM) comparison tools such as Proportional Distance, false positive and false negative, and the favicon image recognition algorithm. There may be a different impact, misclassifying a phishing website than misclassifying a legitimate website if the proportional distance approach is used. To solve this, the proportional distance method is used.