abstract: Despite an abundance of defenses that work to protect Internet users from online threats, malicious actors continue deploying relentless large-scale phishing attacks that target these users. Effectively mitigating phishing attacks remains a challenge for the security community due to attackers' ability to evolve and adapt to defenses, the cross-organizational nature of the infrastructure abused for phishing, and discrepancies between theoretical and realistic anti-phishing systems. Although technical countermeasures cannot always compensate for the human weakness exploited by social engineers, maintaining a clear and up-to-date understanding of the motivation behind---and execution of---modern phishing attacks is essential to optimizing such countermeasures.
In this dissertation, I analyze the state of the anti-phishing ecosystem and show that phishers use evasion techniques, including cloaking, to bypass anti-phishing mitigations in hopes of maximizing the return-on-investment of their attacks. I develop three novel, scalable data-collection and analysis frameworks to pinpoint the ecosystem vulnerabilities that sophisticated phishing websites exploit. The frameworks, which operate on real-world data and are designed for continuous deployment by anti-phishing organizations, empirically measure the robustness of industry-standard anti-phishing blacklists (PhishFarm and PhishTime) and proactively detect and map phishing attacks prior to launch (Golden Hour). Using these frameworks, I conduct a longitudinal study of blacklist performance and the first large-scale end-to-end analysis of phishing attacks (from spamming through monetization). As a result, I thoroughly characterize modern phishing websites and identify desirable characteristics for enhanced anti-phishing systems, such as more reliable methods for the ecosystem to collectively detect phishing websites and meaningfully share the corresponding intelligence. In addition, findings from these studies led to actionable security recommendations that were implemented by key organizations within the ecosystem to help improve the security of Internet users worldwide. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020
| Identifer | oai:union.ndltd.org:asu.edu/item:57029 |
| Date | January 2020 |
| Contributors | Oest, Adam (Author), Ahn, Gail-Joon (Advisor), Doupé, Adam (Advisor), Shoshitaishvili, Yan (Committee member), Johnson, RC (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Doctoral Dissertation |
| Format | 211 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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