Article
A REVIEW OF VARIOUS EFFICIENT TECHNIQUES FOR SECURING COAP-ENABLED IOT NETWORKS
This review explores the evolution of IoT Intrusion Detection Systems (IDS), transitioning from traditional anomaly-based models to advanced, intelligent detection frameworks. It synthesizes findings from fifteen peer-reviewed studies focusing on graph-based neural networks, distributed learning architectures, and resource-efficient optimization techniques. The review highlights innovations like analogical training, generative model creation, ensemble-distillation methods, and protocol-specific detection strategies. It evaluates datasets including CoAP-DoS and other benchmarks to assess detection accuracy. The study concludes that modern IDS technologies are becoming protocol-adaptable and more intellectually robust, aiming to balance computational performance, detection precision, and effective protection across diverse and resource-constrained IoT network environments
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