Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs

Abstract

With the advent of the 20th century, the popularity of digital service usages is increasing every day. The internet has always been a popular communication method, and phishing webpages have been a challenging issue for more than two decades. Especially, E-commerce and other global companies face enormous challenges due to phishing of websites. Many developed countries have reported substantial economic loss due to unwanted phishing activities. With the exponential increase of digital communications, these phishing activities are going to be increased. There is a need for an effective intrinsic phishing detection technique. Phishing websites have some unique features by which they can be identified. In this research, a Light gradient boosting machine-based phishing email detection model using phisher websites' features of mimic URLs has been proposed. The primary objective is to develop a highly secured and accurate model for successful identification of security breach through websites phishing. With the performance comparison of other ensemble as well as state-of-the-art machine learning models, the proposed model resulted high performance accuracy and proved to a robust approach for phishing activity.