Improving the Adversarial Robustness of Machine Learning-based Phishing Website Detectors: An Autoencoder-based Auxiliary Approach
Files
Date
2025-01-07
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
416
Ending Page
Alternative Title
Abstract
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.
Description
Keywords
Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI, adversarial robustness, cybersecurity, graph convolutional autoencoder, machine learning, phishing website detection
Citation
Extent
9
Format
Geographic Location
Time Period
Related To
Proceedings of the 58th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.