Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Author:
Affiliation:
1. SAP Security Research & EURECOM, Mougins, France
2. University of Genova & Pluribus One, Genova, Italy
3. University of Cagliari & Pluribus One, Cagliari, Italy
4. SAP Security Research, Mougins, France
5. EURECOM, Biot, France
Funder
European Union Horizon 2020 Research and Innovation Actions
Fondazione di Sardegna
European Union NextGenerationEU
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3605764.3623920
Reference48 articles.
1. VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity
2. Generating Optimal Attack Paths in Generative Adversarial Phishing
3. Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks
4. SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
5. SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
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1. "Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages;Proceedings of the ACM Web Conference 2024;2024-05-13
2. Understanding the Process of Data Labeling in Cybersecurity;Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing;2024-04-08
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