IoT, Volume 6, Issue 3
2025 September - 23 articles
Cover Story: The exponential growth of Artificial Intelligence of Things (AIoT) devices has amplified the need for secure and privacy-preserving anomaly detection methods, particularly in resource-constrained edge environments. This study introduces a two-stage hybrid federated learning framework combining generative models and histogram-based gradient boosting to detect and classify anomalies in IoT networks. Validated on the N-BaIoT dataset, the proposed approach achieves 99.14% accuracy, ensuring robust data privacy for securing industrial IoT ecosystems. View this paper
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IoT - ISSN 2624-831X