• DocumentCode
    1589607
  • Title

    De-obfuscation and Detection of Malicious PDF Files with High Accuracy

  • Author

    Lu, Xun ; Zhuge, Jianwei ; Wang, Ruoyu ; Cao, Yinzhi ; Chen, Yan

  • fYear
    2013
  • Firstpage
    4890
  • Lastpage
    4899
  • Abstract
    Due to its high popularity and rich functionalities, the Portable Document Format (PDF) has become a major vector for malware propagation. To detect malicious PDF files, the first step is to extract and de-obfuscate Java Script codes from the document, for which an effective technique is yet to be created. However, existing static methods cannot de-obfuscate Java Script codes, existing dynamic methods bring high overhead, and existing hybrid methods introduce high false negatives. Therefore, in this paper, we present MPScan, a scanner that combines dynamic Java Script de-obfuscation and static malware detection. By hooking the Adobe Reader´s native Java Script engine, Java Script source code and op-code can be extracted on the fly after the source code is parsed and then executed. We also perform a multilevel analysis on the resulting Java Script strings and op-code to detect malware. Our evaluation shows that regardless of obfuscation techniques, MPScan can effectively de-obfuscate and detect 98% malicious PDF samples.
  • Keywords
    Cyberspace; Dictionaries; Educational institutions; Engines; Malware; Portable document format; Standards; Dynamic API Hooking; JavaScript De-obfuscation; Op-code Signature Matching;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Sciences (HICSS), 2013 46th Hawaii International Conference on
  • Conference_Location
    Wailea, HI, USA
  • ISSN
    1530-1605
  • Print_ISBN
    978-1-4673-5933-7
  • Electronic_ISBN
    1530-1605
  • Type

    conf

  • DOI
    10.1109/HICSS.2013.166
  • Filename
    6480434