The increasing number of Internet of Things (IoT) devices has made it crucial to protect these networked ecosystems from cyberattacks. To differentiate between legitimate operations and possible intrusions, this collection of network traffic data encompasses a wide range of features, from fundamental connection metrics to intricate interaction patterns. This dataset offers a fundamental tool for scholars and scientists to develop models that can accurately predict, detect, and mitigate undesired cyber activities by concentrating on the distinctive features of smart home network traffic. The aim of predicting smart home intrusion using Machine Learning (ML) Algorithms necessitated the utilization of Stacking Classification (SC), Gradient Boosting Classifier (GBC), and Random Forest Classification (RFC) models in conjunction with Leader Harris Hawks Optimization (LHHO) and Mayfly Optimization Algorithm (MOA) optimizers. In this study, the Stacking C model was employed to categorize the other models under a novel conceptual framework, subsequently integrating them with optimizers to formulate new hybrid models termed SGRLH and HGRMO. Furthermore, the outcomes of the utilized models were compared under 2 conditions, namely Right and Wrong detection. The results reveal that under the right detection condition, the SGRLH model exhibited the highest precision value of 0.997, establishing it as the optimal model, followed by the SGRMO model with a precision value of 0.992, securing the second position, while the SGR model with a precision value of 0.970 occupied the third position.
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