Phishing Website Detection using Machine Learning Techniques

Abstract

Phishing refers to the fraudulent attempt to obtain sensitive information such as a user's username and password, as well as details about a checking account or credit card, for the purpose of using that information for malevolent purposes. It's possible that phishing scams are the most common form of cybercrime utilised today. Phishing attacks can be launched against victims in a variety of contexts, including the online payment industry, webmail, financial institutions, file hosting or cloud storage, and many more. Phishing may be detected quite effectively through the use of machine learning. Additionally, it eliminates the problem that was caused by the prior method. This study focuses on the application of machine learning technology to the problem of identifying phishing URLs. Specifically, it extracts and compares numerous characteristics of real and fraudulent URLs. By utilising the Support Vector Machine technique as well as the Random Forest technique, the project intends to identify URLs that lead to phishing websites.