Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022
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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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Benjamin M. Ampel
Assistant Professor in Computer Information Systems and Director, CyberAI Research and Education Center (CARE)
My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.