Detection of Phishing Websites using Generative Adversarial Network

Robic–Butez, Pierrick and Win, Thu Yein ORCID: 0000-0002-4977-0511 (2020) Detection of Phishing Websites using Generative Adversarial Network. In: 2019 IEEE International Conference on Big Data (Big Data), 9th-12th December 2019, Los Angeles. ISBN 9781728108575

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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%.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Security Analytics; Phishing Detection; Generative Adversarial Networks; Cloud Security; Big Data Analytics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools and Research Institutes > School of Computing and Engineering > Technical & Applied Computing
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 03 Jul 2020 15:31
Last Modified: 03 Jul 2020 15:45
URI: http://eprints.glos.ac.uk/id/eprint/8528

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