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EnglishCurrent 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.
Augmenting phishing squatting detection with GANs / Valentim, Rodolfo; Drago, Idilio; Trevisan, Martino; Cerutti, Federico; Mellia, Marco. - ELETTRONICO. - (2021), pp. 3-4. ((Intervento presentato al convegno ACM CoNEXT 2021 - International Conference on emerging Networking EXperiments and Technologies tenutosi a Virtual Event Germany nel 7 December 2021 [10.1145/3488658.3493787].
| Titolo: | Augmenting phishing squatting detection with GANs | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Abstract: | Current solutions to tackle phishing employ blocklists that are built from user reports or automa...tic 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. | |
| ISBN: | 9781450391337 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 3488658.3493787.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia | |
| _CoNext_Student_2021___Augmenting_Phishing_Domain_Squatting_Training_Dataset_with_GANs.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2943633
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