Finding the Needle: A Study of the PE32 Rich Header and Respective Malware Triage

  • George D. WebsterEmail author
  • Bojan Kolosnjaji
  • Christian von Pentz
  • Julian Kirsch
  • Zachary D. Hanif
  • Apostolis Zarras
  • Claudia Eckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10327)


Performing triage of malicious samples is a critical step in security analysis and mitigation development. Unfortunately, the obfuscation and outright removal of information contained in samples makes this a monumentally challenging task. However, the widely used Portable Executable file format (PE32), a data structure used by the Windows OS to handle executable code, contains hidden information that can provide a security analyst with an upper hand. In this paper, we perform the first accurate assessment of the hidden PE32 field known as the Rich Header and describe how to extract the data that it clandestinely contains. We study 964,816 malware samples and demonstrate how the information contained in the Rich Header can be leveraged to perform rapid triage across millions of samples, including packed and obfuscated binaries. We first show how to quickly identify post-modified and obfuscated binaries through anomalies in the header. Next, we exhibit the Rich Header’s utility in triage by presenting a proof of concept similarity matching algorithm which is solely based on the contents of the Rich Header. With our algorithm we demonstrate how the contents of the Rich Header can be used to identify similar malware, different versions of malware, and when malware has been built under different build environment; revealing potentially distinct actors. Furthermore, we are able to perform these operations in near real-time, less than 6.73 ms on commodity hardware across our studied samples. In conclusion, we establish that this little-studied header in the PE32 format is a valuable asset for security analysts and has a breadth of future potential.


Object File Visual Studio Rapid Triage Generate Source Code Malicious Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank our shepherd Pavel Laskov and the reviewers for their valuable feedback. We are thankful to the Technical University of Munich for providing ample infrastructure to support our development efforts. Additionally, we thank the the German Federal Ministry of Education and Research under grant 16KIS0327 (IUNO) and the Bavarian State Ministry of Education, Science and the Arts as part of the FORSEC research association for providing funding for our infrastructure. We would also like to thank the United States Air Force for sponsoring George Webster in his academic pursuit. Lastly, we would like to thank Microsoft Digital Crimes Unit, VirusTotal, and Yara Exchange for their support and valuable discussions.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • George D. Webster
    • 1
    Email author
  • Bojan Kolosnjaji
    • 1
  • Christian von Pentz
    • 1
  • Julian Kirsch
    • 1
  • Zachary D. Hanif
    • 1
  • Apostolis Zarras
    • 1
  • Claudia Eckert
    • 1
  1. 1.Technical University of MunichMunichGermany

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