Abstract
Internet of Things (IoT) wide applications has significantly increased the need for robust anomaly detection to safeguard against countless security breaches. This paper presents a review that examines the effectiveness of hybrid solutions incorporating supervised and unsupervised machine learning models for enhancing IoT security. The review consolidates insights from a range of studies employing models such as Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), and Gaussian Mixture Models (GMM). It integrates the findings of diverse research, emphasizing improvements in terms of detection accuracy and computational demands. The study delineates challenges in the field to evaluate the efficacy of hybrid techniques and their potential for immediate IoT security applications. Moreover, future research directions encompass the exploration of new algorithms and the integration of these approaches within dynamic IoT data streams.