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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Váldodahkkit:	Rodrigo Castillo Camargo, Juan Murcia Nieto, Nicolás Rojas, Daniel Díaz-López, Santiago Alférez, Angel Luis Perales Gómez, Pantaleone Nespoli, Félix Gómez Mármol, Umit Karabiyik
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	SpringerOpen 2025-04-01
Ráidu:	Cybersecurity
Fáttát:	Malware detection Malware obfuscation Computer vision Transfer learning Deep learning Networking system of artificial intelligence
Liŋkkat:	https://doi.org/10.1186/s42400-025-00396-z
Fáddágilkorat:	Lasit fáddágilkoriid Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!

DEFENDIFY: defense amplified with transfer learning for obfuscated malware framework

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