Detection of Phishing Using Machine Learning algorithms

Abstract

Phishing is a widespread method of tricking individuals into giving up their personal information by utilizing fake websites. Phishing website URLs are designed to steal personal information such as user IDs, pins, and netbanking activities. Phishers employ websites that are aesthetically and semantically indistinguishable to the legitimate ones. As technology advances, phishing strategies have become more sophisticated, necessitating the use of anti-phishing measures to identify phishing. Machine learning is a proficient method for countering phishing attacks. This study examines the characteristics utilized in detection as well as machine learning-based detection approaches. Phishing is infamous amongst hackers, since persuading someone to click a link that appears to be authentic is easier than it is to break past a PC’s defense measures. The links that malicious in the message are built to look like that similar fashion to their organization by utilizing the organization’s web pages and logos, etc. In this project, we’ll explain the characteristics of phishing domains (also known as fraudulent domains), the qualities that separate them from valid domains, why it's necessary to identify these domains, with the usage of machine learning models.