Description
Despite extensive research by the security community, cyberattacks such as phishing and Internet of Things (IoT) attacks remain profitable to criminals and continue to cause substantial damage not only to the victim users that they target, but also the organizations they impersonate. In recent years, phishing websites have taken the place of malware websites as the most prevalent web-based threat. Even though technical countermeasures effectively mitigate web-based malware, phishing websites continue to grow in sophistication and successfully slip past modern defenses. Phishing attack and its countermeasure have entered into a new era, where one side has upgraded their weapon, attempting to conquer the other. In addition, the amount and usage of IoT devices increases rapidly because of the development and deployment of 5G network. Although researchers have proposed secure execution environment, attacks targeting those devices can often succeed. Therefore, the security community desperately needs detection and prevention methodologies to fight against phishing and IoT attacks. In this dissertation, I design a framework, named CrawlPhish, to understand the prevalence and nature of such sophistications, including cloaking, in phishing attacks, which evade detections from the anti-phishing ecosystem by distinguishing the traffic between a crawler and a real Internet user and hence maximize the return-on-investment from phishing attacks. CrawlPhish also detects and categorizes client-side cloaking techniques in phishing with scalability and automation. Furthermore, I focus on the analysis redirection abuse in advanced phishing websites and hence propose mitigations to classify malicious redirection use via machine learning algorithms. Based on the observations from previous work, from the perspective of prevention, I design a novel anti-phishing system called Spartacus that can be deployed from the user end to completely neutralize phishing attacks.
Lastly, inspired by Spartacus, I propose iCore, which proactively monitors the operations in the trusted execution environment to identify any maliciousness.
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Details
Title
- Detection and Prevention of Sophisticated Cyberattacks
Contributors
- Zhang, Penghui (Author)
- Ahn, Gail-Joon (Thesis advisor)
- Doupe, Adam (Thesis advisor)
- Oest, Adam (Committee member)
- Kapravelos, Alexandros (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Resource Type
Collections this item is in
Note
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Partial requirement for: Ph.D., Arizona State University, 2022
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Field of study: Computer Engineering