Development of a Novel Approach to Phishing Detection Using Machine Learning

Abstract

Protecting and preventing sensitive data from being used appropriately has become a challenging task. Even a small mistake in securing data can be exploited by phishing attacks to release private information such as passwords or financial information to a malicious actor. Phishing has now proven so successful; that it is the number one attack vector. Many approaches have been proposed to protect against this cyber-attack, from additional staff training, and enriched spam filters to large collaborative databases of known threats such as PhishTank and OpenPhish. However, they mostly rely upon a user falling victim to an attack and manually adding this new threat to the shared pool, which presents a constant disadvantage in the fight back against phishing. In this paper, we propose a novel approach to protect against phishing attacks using machine learning. Unlike previous work in this field, our approach uses an automated detection process and requires no further user interaction, which allows for a faster and more accurate detection process. The experiment results show that our approach has a high detection rate. Machine Learning is an effective method for detecting phishing. It also eliminates the disadvantages of the previous method. We thoroughly reviewed the literature and suggested a new method for detecting phishing websites using feature extraction and a machine learning algorithm. This research aims to use the dataset collected to train ML models and deep neural nets to anticipate phishing websites.