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dc.contributor.advisorSalinas, Sergio
dc.contributor.authorAkins, Brian C.
dc.date.accessioned2021-06-23T18:40:24Z
dc.date.available2021-06-23T18:40:24Z
dc.date.issued2021-05
dc.identifier.othert21001s
dc.identifier.urihttps://soar.wichita.edu/handle/10057/21579
dc.descriptionThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
dc.description.abstractBotnets are a collection of devices that have been infected with malware to grant control to a cyber attacker over that device. Botnets are primarily operated over the internet, where the devices can contact the cyber attacker to receive instructions. The existence of these botnets can cause massive issues for not only the owner of the device, but for anyone who uses the internet. Network detection algorithms have shown promise in previous works as a possible solution for combating botnets. These detection algorithms search for common botnet activity like domain generation algorithms (DGA) which botnets use to find the web address to contact the cyber attacker. In this paper we will contest one of these network detection algorithms. Our proposal is based on the idea of a Devil’s Advocate. A Devil’s Advocate is a person, or machine learning algorithm in this case, that contests an idea in order to test the strength of the idea. Thus, we propose Devil’s DGA, a Deep Reinforcement Algorithm that searches for a possible modification the attacker can make to avoid a network detection algorithm. Devil’s DGA takes a known DGA and modifies the algorithm’s features to create a new DGA that can avoid detection algorithms. We show that Devil’s DGA is also able to achieve detection rates as low as 1.1% Devil’s DGA is also able to find a means to avoid detection in less than 28 seconds.
dc.format.extentxi, 30 pages
dc.language.isoen_US
dc.publisherWichita State University
dc.rights© Copyright 2021 by Brian C. Akins All Rights Reserved
dc.subject.lcshElectronic dissertations
dc.titleDevil’s DGA
dc.typeThesis


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  • CE Theses and Dissertations
    Doctoral and Master's theses authored by the College of Engineering graduate students
  • EECS Theses and Dissertations
    Collection of Master's theses and Ph.D. dissertations completed at the Dept. of Electrical Engineering and Computer Science
  • Master's Theses
    This collection includes Master's theses completed at the Wichita State University Graduate School (Fall 2005 --)

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