DEFENDIFY: defense amplified with transfer learning for obfuscated malware framework

DEFENDIFY: defense amplified with transfer learning for obfuscated malware framework

Abstract The existence of malicious software (malware) represents a potential threat to users who connect to a large set of services provided by multiple providers. Such malware is capable of stealing, spying on, encrypting data from users, and spreading, provoking impacts that are beyond a single c...

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Bibliographic Details
Main Authors:	Rodrigo Castillo Camargo, Juan Murcia Nieto, Nicolás Rojas, Daniel Díaz-López, Santiago Alférez, Angel Luis Perales Gómez, Pantaleone Nespoli, Félix Gómez Mármol, Umit Karabiyik
Format:	Article
Language:	English
Published:	SpringerOpen 2025-04-01
Series:	Cybersecurity
Subjects:	Malware detection Malware obfuscation Computer vision Transfer learning Deep learning Networking system of artificial intelligence
Online Access:	https://doi.org/10.1186/s42400-025-00396-z
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