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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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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https://doi.org/10.1186/s42400-025-00396-z

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