Name
Abstract | ||
|---|---|---|
ABSTRACTCurrent 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3488658.3493787 | CONEXT |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodolfo V. Valentim | 1 | 0 | 0.34 |
| Idilio Drago | 2 | 0 | 0.68 |
| Martino Trevisan | 3 | 78 | 16.10 |
| Federico Cerutti | 4 | 0 | 0.34 |
| Marco Mellia | 5 | 2748 | 204.65 |