Optimizing Cybersecurity in 5G-Enabled IoT Networks via a Resource-Efficient Random Forest Model
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With the widespread deployment of 5G networks together with many Internets of Things (IoT) devices, the demand for secure space has grown substantially. The proposed research focuses on improving the existing cybersecurity solutions in 5G based IoT networks through resource-efficient implementation of the random forest (RF) model. This study evaluated an IDPS based on a completely simulated 5G-era IoT scenario. The study evaluated an IDPS using a simulated 5G-era IoT environment replicating real-world device interactions. Synthetic datasets representing normal and malicious traffic, including distributed denial-of-service (DDoS) attacks, were used for model training and testing. The performance of the RF model was assessed via important metrics, including accuracy, recall, precision, and the F-measure. The RF model achieved a high F-measure of 77%, reflecting a strong ability to identify and mitigate threats. Additionally, the model performs exceptionally well in terms of essential characteristics such as the identification of anomalies, the ability to respond in real time, the management of resources, and the protection of privacy. Within the context of a 5G network, the findings demonstrate that is random forest an acceptable and effective method for securing resource-constrained Internet of Things networks. Future work may explore hybrid AI models to enhance security capabilities
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