• DocumentCode
    3390977
  • Title

    A compiler classification framework for use in reverse engineering

  • Author

    Torri, Stephen ; Britt, Winard ; Hamilton, J.A., Jr.

  • fYear
    2009
  • fDate
    March 30 2009-April 2 2009
  • Firstpage
    159
  • Lastpage
    166
  • Abstract
    The purpose of this work is to contribute to the reverse engineering of software by providing a means for identifying the type of compiler used to compile a Java class or Linux ELF file. A software framework is presented for extracting potentially useful information from class files and analyzing that information to classify future files. A General Regression Neural Network is implemented and optimized using evolutionary computation. In experimental results, the system can classify compiler type on an file it has not seen before with over 98% accuracy.
  • Keywords
    Java; Linux; evolutionary computation; neural nets; program compilers; regression analysis; reverse engineering; Java class; Linux ELF file; compiler classification framework; evolutionary computation; general regression neural network; reverse engineering; Artificial intelligence; Data mining; Information analysis; Intrusion detection; Libraries; Neural networks; Optimization methods; Program processors; Reverse engineering; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on
  • Conference_Location
    Nashville, TN
  • Print_ISBN
    978-1-4244-2769-7
  • Type

    conf

  • DOI
    10.1109/CICYBS.2009.4925104
  • Filename
    4925104