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arXiv · 2412.09057

PhishIntel: Toward Practical Deployment of Reference-Based Phishing Detection

Yuexin Li·Hiok Kuek Tan·Qiaoran Meng·Mei Lin Lock·Tri Cao·Shumin Deng·Nay Oo·Hoon Wei Lim·Bryan Hooi
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Abstract

Phishing is a critical cyber threat, exploiting deceptive tactics to compromise victims and cause significant financial losses. While reference-based phishing detectors (RBPDs) have achieved notable advancements in detection accuracy, their real-world deployment is hindered by challenges such as high latency and inefficiency in URL analysis. To address these limitations, we present PhishIntel, an end-to-end phishing detection system for real-world deployment. PhishIntel intelligently determines whether a URL can be processed immediately or not, segmenting the detection process into two distinct tasks: a fast task that checks against local blacklists and result cache, and a slow task that conducts online blacklist verification, URL crawling, and webpage analysis using an RBPD. This fast-slow task system architecture ensures low response latency while retaining the robust detection capabilities of RBPDs for zero-day phishing threats. Furthermore, we develop two downstream applications based on PhishIntel: a phishing intelligence platform and a phishing email detection plugin for Microsoft Outlook, demonstrating its practical efficacy and utility.

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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. 2024-12-12. PhishIntel: Toward Practical Deployment of Reference-Based Phishing Detection. https://doi.org/10.1145/3701716.3715192

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