Optimization of code caves in malware binaries to evade machine learning detectors
This research was supported by the Ministerio de Ciencia, Innovación y Universidades (Grant Refs. PGC2018-095322-B-C22 and PID2019-111429RB-C21), by the Region of Madrid grant CYNAMON-CM (P2018/TCS-4566), co-financed by European Structural Funds ESF and FEDER, and the Excellence Program EPUC3M17. Th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/24407 |
| Acceso en línea: | https://hdl.handle.net/10115/24407 |
| Access Level: | acceso abierto |
| Palabra clave: | Malware Evasion Machine learning Adversarial example Genetic algorithm |
| Sumario: | This research was supported by the Ministerio de Ciencia, Innovación y Universidades (Grant Refs. PGC2018-095322-B-C22 and PID2019-111429RB-C21), by the Region of Madrid grant CYNAMON-CM (P2018/TCS-4566), co-financed by European Structural Funds ESF and FEDER, and the Excellence Program EPUC3M17. The opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect those of any of the funders. |
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