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I am a first-year PhD student in Computer Science at National University of Singapore (NUS), advised by Prof. Bryan Hooi and Prof. Jiaheng Zhang. Prior to this, I worked as a Research Associate at the NUS-NCS Joint Lab for Cyber Security for over two years, where I led the research on knowledge-aware phishing detection and its real-world deployment. The outcomes of my research have been successfully translated into a POC system and are on track for commercialization by NCS Singapore [1, 2]. I earned my M.Comp. in Artificial Intelligence from NUS in 2023. Before that, I completed my B.Eng. in Computer Science and Technology at South China University of Technology in 2021, where I did research on evolutionary algorithms and urban traffic computing under Prof. Zhi-Hui Zhan. I was also fortunate to be a visiting student to University College London in 2019 and University of California, Berkeley in 2020. Email: yuexinli [AT] u [DOT] nus [DOT] edu CV / LinkedIn / Google Scholar / Github |
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My research broadly focuses on scam detection (e.g. phishing detection) and the audit of GenAI models (e.g., LLM watermark). I am passionate about advancing the safety, reliability and trustworthiness of cyberspace in the era of GenAI. I always welcome collaborations and open discussions—please feel free to reach out. |
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Yuexin Li, Hiok Kuek Tan, Qiaoran Meng, Mei Lin Lock, Tri Cao, Shumin Deng, Nay Oo, Hoon Wei Lim, Bryan Hooi WWW 2025 (Demo Track) We present PhishIntel, an end-to-end phishing detection system for real-world deployment, with a fast-slow task system architecture that ensures low response latency while retaining the robust detection capabilities of RBPDs for zero-day phishing threats. |
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Tri Cao*, Chengyu Huang*, Yuexin Li*, Huilin Wang, Amy He, Nay Oo, Bryan Hooi AAAI 2025 (Oral) We introduce PhishAgent, a multimodal agent that combines a wide range of tools, integrating both online and offline knowledge bases with multimodal large language models, for phishing detection. |
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Yuexin Li, Chengyu Huang, Shumin Deng, Mei Lin Lock, Tri Cao, Nay Oo, Hoon Wei Lim, Bryan Hooi USENIX Security 2024 KnowPhish Slides Video Webpage Spider cite We propose 1) KnowPhish, a large-scale multimodal brand knowledge base that can be integrated with any reference-based phishing detector (RBPD) in a plug-and-play manner, and 2) KnowPhish Detector (KPD), a multimodal RBPD to detect phishing webpages with or without logos. |
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- CIKM 2025 - Frontiers of Computer Science (FCS) |
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