Generating Adversarial Phishing Websites to Evade Machine Learning-based Anti-Phishing Detectors: A Reinforcement Learning Approach
In Plain Terms
This workshop paper studies how attackers could automatically create phishing websites designed to slip past machine learning detectors. Using a reinforcement learning approach, the authors generate adversarial phishing pages to expose weak spots in current anti-phishing defenses and motivate the design of more robust detection systems.
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Citation
Yang Gao, Sagar Samtani, Hongyi Zhu, Benjamin M. Ampel, & Yidong Chai (2023). Generating Adversarial Phishing Websites to Evade Machine Learning-based Anti-Phishing Detectors: A Reinforcement Learning Approach. WDS
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.