Improving the Adversarial Robustness of Machine Learning-based Phishing Website Detectors: An Autoencoder-based Auxiliary Approach

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dc.contributor.authorGao, Yang
dc.contributor.authorSamtani, Sagar
dc.contributor.authorShah, Ankit
dc.date.accessioned2024-12-26T21:04:46Z
dc.date.available2024-12-26T21:04:46Z
dc.date.issued2025-01-07
dc.description.abstractAnti-phishing research relies on collaboration between defensive and offensive efforts. The defensive side develops machine learning-based phishing website detectors to protect users from phishing attacks. However, adversaries can manipulate detectable phishing websites into evasive ones as adversarial examples, misleading detectors into classifying them as legitimate. Therefore, offensive efforts are vital to examine the threats posed by adversaries and inform the defensive side to improve the adversarial robustness of detectors. Prevailing approaches to improve adversarial robustness may compromise a detector’s original high performance on clean data (nonadversarial websites) as it becomes more accurate at detecting adversarial examples. To address this, we propose a novel approach using a Graph Convolutional Autoencoder as an auxiliary model to make collaborative decisions with the original detector in distinguishing evasive phishing websites from legitimate ones. We evaluate our approach by enhancing a CNN-based detector against adversarial attacks. Our approach achieves high adversarial robustness while maintaining high performance on clean data compared to retraining and fine-tuning benchmarks.
dc.format.extent9
dc.identifier.doi10.24251/HICSS.2025.050
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.othere4fcb857-215c-439b-b8fd-6318d04b27c8
dc.identifier.urihttps://hdl.handle.net/10125/108886
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI
dc.subjectadversarial robustness, cybersecurity, graph convolutional autoencoder, machine learning, phishing website detection
dc.titleImproving the Adversarial Robustness of Machine Learning-based Phishing Website Detectors: An Autoencoder-based Auxiliary Approach
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage416

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