Machine learning-based anomaly detection in IoT Security: A comparative analysis of supervised and unsupervised models

Massive device networks stemming from the rapid growth of Internet of Things devices became a security threat because they expanded exposure to cyberattacks. Security tools from the past show limited capability to detect abnormalities within IoT systems that grow rapidly, so advanced anomaly detection methods must be created. Identifying and detecting IoT network security breaches and malicious activities use machine learning (ML)-based approaches as powerful analytical tools. The work presents a structured overview of machine learning algorithms that monitor IoT security environments using supervised and unsupervised methods. Numerous supervised learning approaches prove successful in detection accuracy since they employ labeled dataset information through decision trees, support vector machines (SVM), and deep learning models. The detection methods experience difficulties when handling emerging security threats. Unsupervised classification tools, autoencoders, and isolation forests detect unknown anomalies well but generally produce numerous false alarms. Two performance indicators evaluate the two methods through an assessment process to determine precision accuracy, system potential, and calculation speed. Security enhancements in the IoT environment become possible by combining supervised and unsupervised learning methods. An end examination of this paper discusses future trends where deep learning unites with federated learning to detect anomalies through real-time edge AI processing.