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Un marco jerárquico de aprendizaje profundo con pérdida doblemente regularizada para la detección robusta de malware
Shatha Abed Alsaedi1, Majed Alwateer1, Nouf Helal Alharbi2
1Department of Computer Science, College of Computer Science and Engineering, Taibah University, 46421, Yanbu, Saudi Arabia.
Scientific reports
|January 6, 2026
Resumen
Este estudio presenta un nuevo marco de aprendizaje profundo para detectar y categorizar malware de Ejecutables Portátiles (PE). Utiliza una nueva función de pérdida para mejorar la precisión y la estabilidad en la detección de amenazas de ciberseguridad.
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