• DocumentCode
    1598044
  • Title

    Header information in malware families and impact on automated classifiers

  • Author

    Walenstein, Andrew ; Hefner, Daniel J. ; Wichers, Jeffery

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Louisiana at Lafayette, Lafayette, LA, USA
  • fYear
    2010
  • Firstpage
    15
  • Lastpage
    22
  • Abstract
    The metadata embedded in program executables provides information that can be useful for automated malware detection or classification. With potentially tens of thousands of variants per malware family, it is unclear how much consistency there is in the metadata, and whether different families exhibit different consistencies. Header information from multiple variants of recent malware was studied to understand the variability of the header information within and among malware families. Classification accuracy extracted using multiple common classifiers showed that, even for rapidly mutating malware families, classifiers trained on header information can outperform ones trained on the program bodies. The results also show that some families have highly consistent header information; this fact suggests limited evolutionary pressure from defense systems. The results indicate that care is needed when evaluating classifiers operating on header as well as program body information.
  • Keywords
    information analysis; invasive software; meta data; pattern classification; automated classifier; automated malware detection; defense system; evolutionary pressure; header information; Accuracy; Data mining; Decision trees; Malware; Niobium; Storms; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Malicious and Unwanted Software (MALWARE), 2010 5th International Conference on
  • Conference_Location
    Nancy, Lorraine
  • Print_ISBN
    978-1-4244-9353-1
  • Type

    conf

  • DOI
    10.1109/MALWARE.2010.5665799
  • Filename
    5665799