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dc.contributor.advisorAhamad, Mustaque
dc.contributor.authorSommers, Allison Kate
dc.date.accessioned2018-08-20T19:11:03Z
dc.date.available2018-08-20T19:11:03Z
dc.date.created2018-05
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/1853/60377
dc.description.abstractIn the realm of this computing age, malware is becoming steadily more prevalent. With the amount of malware samples taken from the wild increasing, malware analysis is becoming increasingly necessary. However, the necessary step of malware analysis is not straightforward, and is often made intentionally more difficult by malware authors. Dynamic sandboxes, often used to analyze wild malware samples, have been used for years as a trusted and necessary component for malware analysis. We expand on the traditional approach to malware analysis by creating a system to provide autonomous, automated assistance for analyzing malware samples. Utilizing virtual machine technology, open-source memory forensics software, and custom scripts in our system, we built our system with the goal of speeding up memory forensics during malware analysis.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectMalware
dc.subjectAnalysis
dc.subjectMemory forensics
dc.titleScalable, Automatic Malware Analysis
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentComputer Science
thesis.degree.levelUndergraduate
dc.contributor.committeeMemberAntonakakis, Manos
dc.date.updated2018-08-20T19:11:03Z


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