Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Anti-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.
Recommended Citation
Gao, Yang; Samtani, Sagar; and Shah, Ankit, "Improving the Adversarial Robustness of Machine Learning-based Phishing Website Detectors: An Autoencoder-based Auxiliary Approach" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 6.
https://aisel.aisnet.org/hicss-58/cl/security/6
Improving the Adversarial Robustness of Machine Learning-based Phishing Website Detectors: An Autoencoder-based Auxiliary Approach
Hilton Waikoloa Village, Hawaii
Anti-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.
https://aisel.aisnet.org/hicss-58/cl/security/6