Cloud-based DDoS detection using hybrid feature selection with deep reinforcement learning (DRL). [PDF]
Sci RepSatpathy S, Tripathy U, Swain PK.
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Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics. [PDF]
Front Artif IntellShameli R, Rajkumar S.
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Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement learning with frilled lizard algorithm. [PDF]
Sci RepMaghrabi LA +6 more
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Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in IoT networks. [PDF]
Sci RepAlharby M.
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PSO-DT based BagDT: a robust lightweight ensemble framework for efficient feature selection and DDoS attack detection in IoT environment. [PDF]
Sci RepShirley JJ, Priya M.
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Dynamic weight clustered federated learning for IoT DDoS attack detection. [PDF]
Sci RepBeshah YK, Abebe SL, Melaku HM.
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Correction: Kaur, N.; Gupta, L. Securing the 6G-IoT Environment: A Framework for Enhancing Transparency in Artificial Intelligence Decision-Making Through Explainable Artificial Intelligence. <i>Sensors</i> 2025, <i>25</i>, 854. [PDF]
Sensors (Basel)Kaur N, Gupta L.
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Optimized ensemble machine learning model for cyberattack classification in industrial IoT. [PDF]
Front Artif IntellAlabdullah B, Sankaranarayanan S.
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Designing a neuro-symbolic dual-model architecture for explainable and resilient intrusion detection in IoT networks. [PDF]
Sci RepAlmadhor A +5 more
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Enhancing SDN security with deep learning and F-balanced cross-entropy for DDoS detection. [PDF]
Sci RepNaeen HM, Ghadamyari M, Barmar M.
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