An Efficient Platform for the Automatic Extraction of Patterns in Native Code
| Autor: | Javier Escalada, Ted Scully, Francisco Ortin |
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| Rok vydání: | 2017 |
| Předmět: |
Source code
Article Subject Computer science media_common.quotation_subject 02 engineering and technology computer.software_genre QA76.75-76.765 Software 0202 electrical engineering electronic engineering information engineering Code (cryptography) Computer software media_common business.industry 020207 software engineering computer.file_format Software quality Computer Science Applications Computer engineering Operating system Malware 020201 artificial intelligence & image processing Binary code Executable business computer Machine code |
| Zdroj: | Scientific Programming, Vol 2017 (2017) Scopus RUO. Repositorio Institucional de la Universidad de Oviedo instname |
| ISSN: | 1875-919X 1058-9244 |
| Popis: | Different software tools, such as decompilers, code quality analyzers, recognizers of packed executable files, authorship analyzers, and malware detectors, search for patterns in binary code. The use of machine learning algorithms, trained with programs taken from the huge number of applications in the existing open source code repositories, allows finding patterns not detected with the manual approach. To this end, we have created a versatile platform for the automatic extraction of patterns from native code, capable of processing big binary files. Its implementation has been parallelized, providing important runtime performance benefits for multicore architectures. Compared to the single-processor execution, the average performance improvement obtained with the best configuration is 3.5 factors over the maximum theoretical gain of 4 factors. |
| Databáze: | OpenAIRE |
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