An Intelligent browser extension for phishing website detection using machine learning models

Abstract

At present, most daily tasks are completed by websites through digital platforms. As a result, website reliability has become a significant concern in today's digital landscape [1]. Phishing is the most common type of cyberattack in today's digital environment [2]. Phishing comes in different forms, such as spear, email, whaling, and vishing [3]. In recent years, there has been an escalation in phishing attacks. One such attack is URL phishing. A URL is a website address that indicates a website's location on a network and how to access it [3]. Most of the phishers create fake websites that appear as legitimate websites. To gather insights for my research, a questionnaire was created to assess the current status of phishing attacks in organizations. The responses indicated that phishing attacks most frequently occur within organizations. Hence, I conducted this research to provide a solution to the ongoing phishing attacks targeting websites. The main objective of this study is to develop a browser extension to detect phishing websites. Additionally, this study focuses on determining the key URL features and the most accurate classification algorithm for training the model. The significance of this study is, it contributes to the increasing need for practical, user-friendly cybersecurity solutions to prevent phishing scams. The current situation highlights the need for advanced security mechanisms to prevent phishing attacks.