Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022Jan 1, 2024·Yang Gao,Benjamin M Ampel,Sagar Samtani· 0 min read CiteTypeJournal articlePublicationDigital Threats: Research and Practice (DTRAP)Last updated on Jun 27, 2025 ← Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach Jan 1, 2024Improving Threat Mitigation Through a Cybersecurity Risk Management Framework: A Computational Design Science Approach Jan 1, 2024 →