Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CICYBS.2009.4925104 | IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN CYBER SECURITY |
Keywords | Field | DocType |
artificial neural networks,linux,evolutionary computation,neural nets,machine learning,software framework,reverse engineering,java,evolutionary computing,regression analysis,indexes,data mining | Functional compiler,Programming language,Computer science,Compiler correctness,Reverse engineering,Evolutionary computation,Compiler,Software,Java,Software framework | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Torri | 1 | 0 | 0.34 |
| Winard Britt | 2 | 0 | 0.34 |
| John A. Hamilton , Jr | 3 | 35 | 8.12 |