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    Blending Fuzzing and Symbolic Execution for Malware Analysis

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    AMIRI-UNDERGRADUATERESEARCHOPTIONTHESIS-2017.pdf (157.4Kb)
    Date
    2017-05
    Author
    Amiri, Addison O.
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    Abstract
    Malware 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.
    URI
    http://hdl.handle.net/1853/58499
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    • School of Computer Science Undergraduate Research Option Theses [205]
    • Undergraduate Research Option Theses [862]

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