Automated Hyperparameter Optimization for Cyberattack Detection Based on Machine Learning in IoT Systems

The growing sophistication of cyberattacks in Internet of Things (IoT) environments demands proactive and efficient solutions. We present an automated hyperparameter optimization (HPO) method for detecting cyberattacks in IoT that explicitly addresses class imbalance. The approach combines a Random Forest surrogate, a UCB acquisition function with controlled exploration, and an objective function that maximizes weighted F1 and MCC; it also integrates stratified validation and a compact selection of descriptors by metaheuristic consensus. Five models (RandomForest, AdaBoost, DecisionTree, XGBoost, and MLP) were evaluated on CICIoT2023 and CIC-DDoS2019. The results show systematic improvements over default configurations and competitiveness compared to Hyperopt and GridSearch. For RandomForest, marked increases were observed in CIC-DDoS2019 (F1-Score from 0.9469 to 0.9995; MCC from 0.9284 to 0.9986) and consistent improvements in CICIoT2023 (F1-Score from 0.9947 to 0.9954; MCC from 0.9885 to 0.9896), while maintaining low inference times. These results demonstrate that the proposed HPO offers a solid balance between performance, computational cost, and traceability, and constitutes a reproducible alternative for strengthening cybersecurity mechanisms in IoT environments with limited resources.