PDGAN: Phishing Detection With Generative Adversarial Networks

Abstract

Phishing is a harmful online attack that could lead to identity theft and financial damages. The demand for high-accuracy phishing detection tools has risen due to the increase of online electronic services and payment systems. Most phishing detection techniques depend on features related to webpage content, which necessitates crawling the webpage and relying on third-party services. Relying on features related to webpage content could not provide high detection accuracy and leads to high false detection rates. Recently, deep learning has become a popular approach for detecting phishing websites. However, limited attention has been given to the generative adversarial network (GAN). This paper proposes a phishing detection model called PDGAN that depends only on a website’s uniform resource locator (URL) to achieve reliable performance. We use a long short-term memory network (LSTM) network as a generator of synthetic phishing URLs and a convolutional neural network (CNN) as a discriminator to decide whether the URLs are phishing or legitimate. We use a dataset containing nearly two million phishing and legitimate URLs obtained through PhishTank and DomCop. The experimental results show that the PDGAN achieves a detection accuracy of 97.58% and a precision of 98.02% without depending on third-party services and with greater accuracy than the state-of-the-art models.