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

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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Bibliographic Details
Main Authors: Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Software-defined networks (SDNs)
Malware detection
Federated learning
Autonomous cybersecurity
Scalable network defense
Privacy-preserving AI
Online Access:https://doi.org/10.1038/s41598-025-14512-z
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https://doi.org/10.1038/s41598-025-14512-z

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