Visible to the public Detection of Phishing websites using Generative Adversarial Network

TitleDetection of Phishing websites using Generative Adversarial Network
Publication TypeConference Paper
Year of Publication2019
AuthorsRobic-Butez, Pierrick, Win, Thu Yein
Conference Name2019 IEEE International Conference on Big Data (Big Data)
PublisherIEEE
ISBN Number978-1-7281-0858-2
Keywordsattack vector, Big Data analytics, Cloud Security, Computer crime, discriminator network, external metadata, feature extraction, Gallium nitride, generative adversarial network, generative adversarial networks, generator network, Generators, hacking endeavour, Human Behavior, human factors, internal structure, low-risk rightreward nature, meta data, neural nets, normal Websites, pattern classification, phishing, phishing datasets, Phishing Detection, phishing Websites, pubcrawl, security analytics, synthetic phishing features, Training, Uniform resource locators, Web sites
Abstract

Phishing is typically deployed as an attack vector in the initial stages of a hacking endeavour. Due to it low-risk rightreward nature it has seen a widespread adoption, and detecting it has become a challenge in recent times. This paper proposes a novel means of detecting phishing websites using a Generative Adversarial Network. Taking into account the internal structure and external metadata of a website, the proposed approach uses a generator network which generates both legitimate as well as synthetic phishing features to train a discriminator network. The latter then determines if the features are either normal or phishing websites, before improving its detection accuracy based on the classification error. The proposed approach is evaluated using two different phishing datasets and is found to achieve a detection accuracy of up to 94%.

URLhttps://ieeexplore.ieee.org/document/9006352
DOI10.1109/BigData47090.2019.9006352
Citation Keyrobic-butez_detection_2019