An Approach to Detect Phishing Websites with Features Selection Method and Ensemble Learning

Abstract

Nowadays, phishing is a major problem on a global scale. Everyone must use the internet in today's society in order to cope up in the real world. As a result, internet crime like phishing has become a serious issue throughout the world. This type of crime can be committed by anyone; all they need is a computer. Additionally, hacking may now be learned quickly by anyone with programming and mathematical skills. The adoption of various techniques by anti-phishing toolbars, such as machine learning, may enable users to quickly identify a fake website. As a result, researchers are now particularly interested in the problem of detecting fraudulent websites. Machine learning techniques have been offered throughout the entire process to more precisely identify fraudulent websites. To find the best accurate outcome, classification with random parameter tuning and ensemble based approaches are utilized. A user-friendly interface has also been suggested to make the system more accessible to the public.

Ensemble Learning-Powered URL Phishing Detection: A Performance Driven Approach

“…In [28], the phishing web detection method adopts custom selection and machine learning using DS-30 and DS-50 data. These techniques include data collection, preprocessing, wrapper removal, embedding, correlation coefficient, data boosting, and chi-square methods, as well as machine learning algorithms using random forest, optical gradient boosting, and class boosting.…”