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Document Type

Article

Keywords

IoT Security, Cyber-Attack, Grey Wolf Optimizer, Feature Selection, SMOTE, Random Forest, XGBoost, CatBoost

Abstract

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrate that GWO reduces features from 32 to 21, thereby enhancing computational efficiency and interpretability without compromising accuracy, while customized SMOTE addresses class imbalance and enhances minority-class detection. The optimized RF and XGBoost models were assessed using accuracy, precision, recall, and F1-score metrics, and achieved 100% accuracy with strong generalization. These results highlight the effectiveness of optimization-based feature selection and data balancing in improving IoT security that is extensible to deep learning and ensemble-based approaches.

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