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dc.contributor.advisorLee, Wenke
dc.contributor.authorAmiri, Addison O.
dc.date.accessioned2017-07-28T18:33:42Z
dc.date.available2017-07-28T18:33:42Z
dc.date.created2017-05
dc.date.submittedMay 2017
dc.identifier.urihttp://hdl.handle.net/1853/58499
dc.description.abstractMalware infections have grown at least five-fold in the past five years. With an increase in IoT devices that are lacking built-in security, this problem is likely to only continue growing. Malware analysis, meanwhile, is becoming ever more challenging. Where manual analysis, symbolic execution, or fuzzing alone are overly time consuming or unfruitful, a combination of these techniques may offer promising solutions. This paper suggests a combination of fuzzing and symbolic execution to reverse engineer malware. A framework is described to tie these components together, producing test cases that call all functionality of a malware binary. These test cases show researchers the protocol used by the malware, as well as its capabilities, and allow for a reconstruction of the C&C server as desired. The goal of this work is to allow researchers to better understand malware and how to effectively combat it.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectMalware analysis
dc.subjectFuzzing
dc.subjectSymbolic execution
dc.titleBlending Fuzzing and Symbolic Execution for Malware Analysis
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentComputer Science
thesis.degree.levelUndergraduate
dc.contributor.committeeMemberChung, Simon P.
dc.date.updated2017-07-28T18:33:42Z


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