Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks

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3375708.3380315 (2).pdf (1.09 MB)

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https://dl.acm.org/doi/abs/10.1145/3375708.3380315

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https://doi.org/10.1145/3375708.3380315
 http://hdl.handle.net/11603/18032

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UMBC Information Systems Department
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2020-03-18

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conference papers and proceedings postprints
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AlEroud, Ahmed; Karabatis, George; Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks; IWSPA '20: Proceedings of the Sixth International Workshop on Security and Privacy AnalyticsMarch 2020 Pages 53–60; https://dl.acm.org/doi/abs/10.1145/3375708.3380315

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Abstract

The URL components of web addresses are frequently used in creating phishing detection techniques. Typically, machine learning techniques are widely used to identify anomalous patterns in URLs as signs of possible phishing. However, adversaries may have enough knowledge and motivation to bypass URL classification algorithms by creating examples that evade classification algorithms. This paper proposes an approach that generates URL-based phishing examples using Generative Adversarial Networks. The created examples can fool Blackbox phishing detectors even when those detectors are created using sophisticated approaches such as those relying on intra-URL similarities. These created instances are used to deceive Blackbox machine learning-based phishing detection models. We tested our approach using actual phishing datasets. The results show that GAN networks are very effective in creating adversarial phishing examples that can fool both simple and sophisticated machine learning phishing detection models.