Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in IoT networks. [PDF]
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Federated Learning and Data Mining-Based Botnet Attack Detection Framework for Internet of Things. [PDF]
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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]
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Dynamic weight clustered federated learning for IoT DDoS attack detection. [PDF]
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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]
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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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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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A deep reinforcement based echo state network for network intrusion classification. [PDF]
PLoS OneAlam K, Bhuiyan MH, Farid DM.
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Correction: Okey et al. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. <i>Sensors</i> 2022, <i>22</i>, 7409. [PDF]
Sensors (Basel)Okey OD +6 more
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