Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022
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In Plain Terms
Anti-phishing tools that use machine learning can often be fooled. In a 2022 security competition, the authors took ten real phishing web pages and disguised their code so that detection models would mistake them for legitimate sites, placing third overall. The write-up shares their techniques and lessons to help others build phishing detectors that are harder to trick.
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Cite this
APA Gao, Y., Ampel, B. M., & Samtani, S. (2024). Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022. Digital Threats: Research and Practice, 5(1), Article 7. https://doi.org/10.1145/3603507
BibTeX
@article{gao2024evading,
title={Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022},
author={Yang Gao and Benjamin M. Ampel and Sagar Samtani},
journal={Digital Threats: Research and Practice},
year={2024},
doi={10.1145/3603507}
}
Benjamin M. Ampel
Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)
My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.