• DocumentCode
    2510002
  • Title

    File Fragment Classification-The Case for Specialized Approaches

  • Author

    Roussev, Vassil ; Garfinkel, Simson L.

  • fYear
    2009
  • fDate
    21-21 May 2009
  • Firstpage
    3
  • Lastpage
    14
  • Abstract
    Increasingly advances in file carving, memory analysis and network forensics requires the ability to identify the underlying type of a file given only a file fragment. Work to date on this problem has relied on identification of specific byte sequences in file headers and footers, and the use of statistical analysis and machine learning algorithms taken from the middle of the file. We argue that these approaches are fundamentally flawed because they fail to consider the inherent internal structure in widely used file types such as PDF, DOC, and ZIP. We support our argument with a bottom-up examination of some popular formats and an analysis of TK PDF files. Based on our analysis, we argue that specialized methods targeted to each specific file type will be necessary to make progress in this area.
  • Keywords
    file organisation; TK PDF files; byte sequences; file carving; file footers; file fragment classification; file headers; machine learning algorithms; memory analysis; network forensics; statistical analysis; Clustering algorithms; Computer science; Conferences; Containers; Digital forensics; Frequency; Histograms; Machine learning algorithms; Statistical analysis; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systematic Approaches to Digital Forensic Engineering, 2009. SADFE '09. Fourth International IEEE Workshop on
  • Conference_Location
    Berkeley, CA
  • Print_ISBN
    978-0-7695-3792-4
  • Type

    conf

  • DOI
    10.1109/SADFE.2009.21
  • Filename
    5341545