期刊名称：International Journal of Computer Networks & Communications
印刷版ISSN：0975-2293
电子版ISSN：0974-9322
出版年度：2020
卷号：12
期号：5
页码：41-54
DOI：10.5121/ijcnc.2020.12503
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
