I'm SPARTACUS, No, I'm SPARTACUS: Proactively Protecting Users from Phishing by Intentionally Triggering Cloaking Behavior
Penghui Zhang, Zhibo Sun, Sukwha Kyung, Hans Walter Behrens, Zion Leonahenahe Basque, Haehyun Cho, Adam Oest, Ruoyu Wang, Tiffany Bao, Yan Shoshitaishvili, Gail-Joon Ahn, Adam Doupé
摘要
Phishing is a ubiquitous and increasingly sophisticated online threat. To evade mitigations, phishers try to "cloak" malicious content from defenders to delay their appearance on blacklists, while still presenting the phishing payload to victims. This catand-mouse game is variable and fast-moving, with many distinct cloaking methods-we construct a dataset identifying 2,933 realworld phishing kits that implement cloaking mechanisms. These kits use information from the host, browser, and HTTP request to classify traffic as either anti-phishing entity or potential victim and change their behavior accordingly. In this work we present Spartacus, a technique that subverts the phishing status quo by disguising user traffic as anti-phishing entities. These intentional false positives trigger cloaking behavior in phishing kits, thus hiding the malicious payload and protecting the user without disrupting benign sites. To evaluate the effectiveness of this approach, we deployed Spartacus as a browser extension from November 2020 to July 2021. During that time, Spartacus browsers visited 160,728 reported phishing URLs in the wild. Of these, Spartacus protected against 132,247 sites (82.3%). The phishing kits which showed malicious content to Spartacus typically did so due to ineffective cloakingthe majority (98.4%) of the remainder were detected by conventional anti-phishing systems such as Google Safe Browsing or VirusTotal, and would be blacklisted regardless. We further evaluate Spartacus
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引用它的顶会 Paper2
- Doubly Dangerous: Evading Phishing Reporting Systems by Leveraging Email Tracking TechniquesAnish Chand, Nick Nikiforakis, Phani VadrevuUSENIX Security 2025
- Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway ScamsXigao Li, Anurag Yepuri, Nick NikiforakisNDSS 2023
它引用的顶会 Paper12
- Data Breaches, Phishing, or Malware?: Understanding the Risks of Stolen CredentialsKurt Thomas, Frank Li, Ali Zand, Jacob Barrett 等CCS 2017 · 被引用 248 次
- Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing WebpagesYun Lin, Ruofan Liu, Dinil Mon Divakaran, Jun Yang Ng 等USENIX Security 2021 · 被引用 164 次
- PhishFarm: A Scalable Framework for Measuring the Effectiveness of Evasion Techniques against Browser Phishing BlacklistsAdam Oest, Yeganeh Safaei, Adam Doupé, Gail-Joon Ahn 等S&P 2019 · 被引用 129 次
- Detecting and Characterizing Lateral Phishing at ScaleGrant Ho, Asaf Cidon, Lior Gavish, Marco Schweighauser 等USENIX Security 2019 · 被引用 113 次
- Cognitive Triaging of Phishing AttacksAmber van der Heijden, Luca AllodiUSENIX Security 2019 · 被引用 100 次
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