PhishIntel: Toward Practical Deployment of Reference-Based Phishing Detection
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arXiv
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| Format: | Preprint |
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2024
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| author | Li, Yuexin Tan, Hiok Kuek Meng, Qiaoran Lock, Mei Lin Cao, Tri Deng, Shumin Oo, Nay Lim, Hoon Wei Hooi, Bryan |
| author_facet | Li, Yuexin Tan, Hiok Kuek Meng, Qiaoran Lock, Mei Lin Cao, Tri Deng, Shumin Oo, Nay Lim, Hoon Wei Hooi, Bryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09057 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PhishIntel: Toward Practical Deployment of Reference-Based Phishing Detection Li, Yuexin Tan, Hiok Kuek Meng, Qiaoran Lock, Mei Lin Cao, Tri Deng, Shumin Oo, Nay Lim, Hoon Wei Hooi, Bryan Cryptography and Security 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. |
| title | PhishIntel: Toward Practical Deployment of Reference-Based Phishing Detection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2412.09057 |