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CrawlPhish: Large-scale analysis of client-side cloaking techniques in phishing

Authors
Zhang, P.Oest, A.Cho, H.Sun, Z.Johnson, R.C.Wardman, B.Sarker, S.Kapravelos, A.Bao, T.Wang, R.Shoshitaishvili, Y.Doupe, A.Ahn, G.-J.
Issue Date
May-2021
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Cloaking; Evasion; JavaScript; Phishing; Web-Security
Citation
Proceedings - IEEE Symposium on Security and Privacy, v.2021-May, pp.1109 - 1124
Journal Title
Proceedings - IEEE Symposium on Security and Privacy
Volume
2021-May
Start Page
1109
End Page
1124
URI
http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/41617
DOI
10.1109/SP40001.2021.00021
ISSN
1081-6011
Abstract
Phishing is a critical threat to Internet users. Although an extensive ecosystem serves to protect users, phishing websites are growing in sophistication, and they can slip past the ecosystem's detection systems - and subsequently cause real-world damage - with the help of evasion techniques. Sophisticated client-side evasion techniques, known as cloaking, leverage JavaScript to enable complex interactions between potential victims and the phishing website, and can thus be particularly effective in slowing or entirely preventing automated mitigations. Yet, neither the prevalence nor the impact of client-side cloaking has been studied.In this paper, we present CrawlPhish, a framework for automatically detecting and categorizing client-side cloaking used by known phishing websites. We deploy CrawlPhish over 14 months between 2018 and 2019 to collect and thoroughly analyze a dataset of 112, 005 phishing websites in the wild. By adapting state-of-the-art static and dynamic code analysis, we find that 35, 067 of these websites have 1, 128 distinct implementations of client-side cloaking techniques. Moreover, we find that attackers' use of cloaking grew from 23.32% initially to 33.70% by the end of our data collection period. Detection of cloaking by our framework exhibited low false-positive and false-negative rates of 1.45% and 1.75%, respectively. We analyze the semantics of the techniques we detected and propose a taxonomy of eight types of evasion across three high-level categories: User Interaction, Fingerprinting, and Bot Behavior.Using 150 artificial phishing websites, we empirically show that each category of evasion technique is effective in avoiding browser-based phishing detection (a key ecosystem defense). Additionally, through a user study, we verify that the techniques generally do not discourage victim visits. Therefore, we propose ways in which our methodology can be used to not only improve the ecosystem's ability to mitigate phishing websites with client-side cloaking, but also continuously identify emerging cloaking techniques as they are launched by attackers. © 2021 IEEE.
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