Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches

Abstract This paper proposes a scalable and autonomous malware detection and defence architecture in software-defined networks (SDNs) that employs federated learning (FL). This architecture combines SDN’s centralized management of potentially significant data streams with FL’s decentralized, privacy...

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Auteurs principaux:	Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2025-08-01
Collection:	Scientific Reports
Sujets:	Software-defined networks (SDNs) Malware detection Federated learning Autonomous cybersecurity Scalable network defense Privacy-preserving AI
Accès en ligne:	https://doi.org/10.1038/s41598-025-14512-z
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