This study conducts a comparative analysis of anomaly detection techniques for enhancing security in Internet of Things (IoT) environments. With the proliferation of IoT devices, ensuring robust security measures against emerging threats is imperative. Leveraging adaptive machine learning approaches, we evaluate the effectiveness of various anomaly detection techniques in mitigating IoT-specific security risks. Key techniques examined include statistical methods, clustering algorithms, and deep learning models. Through comprehensive experimentation and evaluation, this research assesses the performance, accuracy, and scalability of each technique in detecting anomalous behavior and potential threats within IoT networks. The study emphasizes the importance of adaptive machine learning for continuously evolving IoT security challenges and proposes recommendations for implementing effective anomaly detection systems tailored to IoT environments.

Keywords: Anomaly Detection, IoT Security, Adaptive Machine Learning, Threat Detection, Comparative Study.