CrawlPhish: Large-Scale Analysis of Client-Side Cloaking Techniques in Phishing

被引：11

作者：

Zhang, Penghui ^{[1
,2
]}

Oest, Adam ^{[3
]}

Cho, Haehyun ^{[4
]}

Sun, Zhibo ^{[1
]}

Johnson, R. C. ^{[3
]}

Wardman, Brad ^{[5
]}

Sarker, Shaown ^{[6
]}

Kapravelos, Alexandros ^{[6
]}

Bao, Tiffany ^{[1
]}

Wang, Ruoyu ^{[1
]}

Shoshitaishvili, Yan ^{[1
]}

Doupe, Adam ^{[7
]}

Ahn, Gail-Joon ^{[8
]}

机构：

[1] Arizona State Univ, Tempe, AZ 85287 USA

[2] Meta Platforms Inc, Menlo Pk, CA 94025 USA

[3] Pay Pal Inc, Scottsdale, AZ USA

[4] Soongsil Univ, Cyber Security Res Ctr, Sch Software, Seoul 06978, South Korea

[5] Pay Pal Inc, Informat Secur, Scottsdale, AZ 85258 USA

[6] North Carolina State Univ, Raleigh, NC 27695 USA

[7] Arizona State Univ, Sch Comp & Augmented Intelligence, Tempe, AZ 85281 USA

[8] Arizona State Univ, Future Comp Lab, Ira A Fulton Sch Engn, Tempe, AZ 85281 USA

来源：

IEEE SECURITY & PRIVACY | 2022年 / 20卷 / 02期

基金：

新加坡国家研究基金会; 美国国家科学基金会;

关键词：

D O I：

10.1109/MSEC.2021.3129992

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Phishing websites with advanced evasion techniques are a critical threat to Internet users because they delay detection by current antiphishing systems. We present CrawlPhish, a framework for automatically detecting and categorizing the client-side (e.g., JavaScript) evasion used by phishing websites.

引用

页码：10 / 21

页数：12

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