Enhancing IoT Device Classification with Hybrid Stacked Ensembles of Machine Learning Classifiers
Keywords:
IoT Device Classification, Hybrid Stacked Ensembles, Machine Learning Classifiers, Random For, XGBoost, Accuracy EnhancementAbstract
Due to the rapid proliferation of Internet of Things (IoT) devices, there have been challenges in their classification. To increase the IoT device classification's precision, here proposed a novel approach that combines the capabilities of several ML classifiers. The model's first development phaseinvolves gathering a well-balanced and diverse dataset that includes various IoT device samples from different sources. The researchers then decided to integrate the learning strategy using an ensemble learning framework. The combination of XGBoost and Random Forest's strengths allowed to perform well with the other ML classifiers. The research focused on optimizing the performance of the classifiers through data preprocessing and engineering. It also utilized cross-validation methods to fine-tune the models. Doing so prevented overfitting and generalized the results. The proposed model was evaluated using a standard benchmark, and its performance was compared with that of modern top-of-the line ensembles and individual classifiers, as well as other state-of-art methods. The results of the comparison revealed that the hybrid-stacked model performed remarkably well at over 93.62% accuracy. To ensure the model's generalizability and practicality, the research utilized a hybrid-stacked ensemble to perform prediction on new data. The MLP classifier was used for this purpose. The findings of the evaluation reinforced the proposed model's accuracy and its potential for practical implementation. The findings of the study revealed that the hybrid structure of the RF and XGBoost, performed well in the classification of various IoT devices. 93.62% accuracy rate indicated the importance of using ensembles in improving the performance of these systems. The study's findings have important implications for various sectors, such as healthcare, smart homes, and industrial automation, where the accuracy of identifying IoT devices is crucial.
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