Hybrid Optimization and Machine Learning Models for IoT Intrusion Detection in Smart Homes
Abstract
The rapid technological growth of IoT devices has significantly enhanced connectivity and usability in smart homes. However, these advantages come with substantial security risks. This study presents a hybrid machine learning approach to predict smart home intrusions using two base classifiers Random Forest Classification (RFC) and Decision Tree Classification (DTC) optimized with Dandelion Optimization (DO) and Rhizostoma Optimization Algorithm (ROA). These combinations result in four hybrid models: RFDO (RFC + DO), RFRO (RFC + ROA), DTDO (DTC + DO), and DTRO (DTC + ROA). A benchmark intrusion detection dataset tailored for smart home environments was used to train and evaluate these models. Among all models, DTRO achieved the highest accuracy of 0.981 during testing, followed by RFRO with 0.977, while the base RFC model performed the worst with 0.915, outperforming the other models in terms of precision, recall, F1-score, and Matthews correlation coefficient. This comparative analysis shows that integrating optimization techniques into traditional classifiers significantly enhances intrusion detection capabilities in resource-constrained IoT environments. The proposed models are suitable for real-time applications in smart home cybersecurity.References
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