scat: Learning from a Single Execution of a Binary

Autor: Laurent Mounier, Franck de Goër, Christopher Ferreira
Přispěvatelé: Validation de Systèmes, Composants et Objets logiciels (VASCO), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), VERIMAG (VERIMAG - IMAG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ANR-15-IDEX-0002,UGA,IDEX UGA(2015)
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)
IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2017, Klagenfurt, Austria
SANER
Popis: International audience; Retrieving information from a binary code is required in several application domains such as system integration or security analysis. Providing tools to help engineers in this task is therefore an important need. We present in this paper scat, an open-source toolbox, relying on lightweight runtime instru-mentation to infer source-level and behavioral information from a binary code, like function prototypes or data-flow relations. We explain the functioning principle of this toolbox, and we give some results obtained on real examples to show its effectiveness.
Databáze: OpenAIRE