Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors

Abstract

Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks recently proposed have demonstrated limited effectiveness due to their lack of optimizing the usage of the adopted manipulations, and they focus solely on specific elements of the HTML code. In this work, we overcome these limitations by first designing a novel set of fine-grained manipulations which allow to modify the HTML code of the input phishing webpage without compromising its maliciousness and visual appearance, i.e., the manipulations are functionality- and rendering-preserving by design. We then select which manipulations should be applied to bypass the target detector by a query-efficient black-box optimization algorithm. Our experiments show that our attacks are able to raze to the ground the performance of current state-of-the-art ML-PWD using just 30 queries, thus overcoming the weaker attacks developed in previous work, and enabling a much fairer robustness evaluation of ML-PWD.

arXiv commentProceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISec '23), November 30, 2023, Copenhagen, Denmark

References in corpus (8)

• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
• Towards Transferable Adversarial Attack against Deep Face Recognition
• Improving Black-box Adversarial Attacks with a Transfer-based Prior
• Functionality-preserving Black-box Optimization of Adversarial Windows Malware
• Towards Benchmark Datasets for Machine Learning Based Website Phishing Detection: An experimental study
• WAF-A-MoLE: Evading Web Application Firewalls through Adversarial Machine Learning
• Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
• Certified Training: Small Boxes are All You Need

Cited by in corpus (2)

• "Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages
• Understanding the Process of Data Labeling in Cybersecurity