A Lightweight Federated Deep Learning Framework for Real-Time Intrusion Detection and Autonomous Mitigation in Software-Defined Wireless Networks
This paper proposes a lightweight, secure framework for Software-Defined Wireless Networks that integrates Zero Michaelis-Menten Knowledge Proof authentication, hash verification, and an enhanced Federated Reinforcement First Synchroton Learning approach with Gated Symmetrical Gaussian Contrastive Regularization Recurrent Units to achieve 99.9833% accuracy in real-time DDoS detection and autonomous mitigation.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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