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Title
Trapped in Transparency: Analyzing the Effectiveness of Security Defenses in Real-World Scenarios
Description
Honeypots – cyber deception technique used to lure attackers into a trap. They contain fake confidential information to make an attacker believe that their attack has been successful. One of the prerequisites for a honeypot to be effective is that it needs to be undetectable. Deploying sniffing and event logging tools alongside the honeypot also helps understand the mindset of the attacker after successful attacks. Is there any data that backs up the claim that honeypots are effective in real life scenarios? The answer is no.Game-theoretic models have been helpful to approximate attacker and defender actions in cyber security. However, in the past these models have relied on expert- created data. The goal of this research project is to determine the effectiveness of honeypots using real-world data. So, how to deploy effective honeypots? This is where honey-patches come into play. Honey-patches are software patches designed to hinder the attacker’s ability to determine whether an attack has been successful or not. When an attacker launches a successful attack on a software, the honey-patch transparently redirects the attacker into a honeypot. The honeypot contains fake information which makes the attacker believe they were successful while in reality they were not.
After conducting a series of experiments and analyzing the results, there is a clear indication that honey-patches are not the perfect application security solution having both pros and cons.
Date Created
2022
Contributors
- Chauhan, Purv Rakeshkumar (Author)
- Doupe, Adam (Thesis advisor)
- Bao, Youzhi (Committee member)
- Wang, Ruoyu (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
39 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.171778
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2022
Field of study: Computer Science
System Created
- 2022-12-20 06:19:18
System Modified
- 2022-12-20 06:19:18
- 1 year 10 months ago
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