Enhancing IoT security through advanced data modeling and machine learning: A framework for threat detection and anomaly prevention

The fast growth of Internet of Things technology has revealed vital infrastructure security weaknesses, making these systems vulnerable to cyber-attacks. Rule-based intrusion detection systems from traditional security fail to adjust to changing threats in IoT-based attacks. The research develops an enhanced security framework that unites data modeling systems with machine learning methods to boost IoT network threat discovery and anomaly defense. The approach analyzes traffic patterns through predictive models and deploys near-synchronized threat response initiatives. The evaluation of the proposed model demonstrates its success with real IoT datasets by lowering false positives and achieving better intrusion detection results. Security experts rely on research into the Mirai botnet attack and Stuxnet worm incidents to validate the basic need for intelligent security systems. Analysis reveals that AI-based security solutions boost IoT protection by effectively fighting new cyber threats, continuing to operate with system efficiency, and preserving operational integrity.