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On the Application of Malware Clustering for Threat Intelligence Synthesis


Abstract Malware forensics is a time-consuming process that involves a significant amount of data collection. To ease the load on security analysts, many attempts have been made to automate the intelligence gathering process and provide a centralized search interface. Certain of these solutions map existing relations between threats and can discover new intelligence by identifying correlations in the data. However, such systems generally treat each unique malware sample as its own distinct threat. This fails to model the real malware landscape, in which so many ``new" samples are actually variants of samples that have already been discovered. Were there some way to reliably determine whether two malware samples belong to the same family, intell... (more)
Created Date 2017-05
Contributor Smith, Joshua Michael (Author) / Ahn, Gail-Joon (Thesis Director) / Zhao, Ziming (Committee Member) / School of Mathematical and Statistical Sciences / Computer Science and Engineering Program / Computer Science and Engineering Program / Barrett, The Honors College
Subject Clustering / Threat Intelligence / Malware Analysis
Series Academic Year 2016-2017
Type Text
Extent 17 pages
Language English
Rights All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


  Thesis.pdf
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