Title :
A compiler classification framework for use in reverse engineering
Author :
Torri, Stephen ; Britt, Winard ; Hamilton, J.A., Jr.
fDate :
March 30 2009-April 2 2009
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;
Conference_Titel :
Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2769-7
DOI :
10.1109/CICYBS.2009.4925104
