Augmenting phishing squatting detection with GANs
Rodolfo Valentim, Idílio Drago, Martino Trevisan, Federico Cerutti, Marco Mellia · 2021
Current solutions to tackle phishing employ blocklists that are built from user reports or automatic approaches. They, however, fall short in detecting zero-day phishing attacks. We propose the use of Generative Adversarial Networks (GANs) to automate the generation of new squatting candidates starting from a list of benign URLs. The candidates can be either manually verified or become part of a training set for existing machine learning models. Our results show that GANs can produce squatting candidates, some of which are previously unknown existing phishing domains.