Search for a command to run...
The growing adoption of Internet of Things (IoT) devices across industrial facilities, medical institutions, intelligent homes, and urban systems has proportionally increased the risk of cyberattacks and unauthorized access. Traditional, centrally controlled security systems often struggle to manage the substantial quantity, variable types, and sensitive nature of information produced by IoT networks, especially while striving to protect user privacy and maintain operational efficiency. This research presents an IoT security model based on federated learning. This model utilizes distributed machine learning, combined with real-time anomaly detection, to strengthen protection against digital threats. Individual IoT devices autonomously refine anomaly detection models utilizing data generated locally. Importantly, only model updates, rather than the original data, are transmitted to a central server. This enables the creation of a comprehensive threat detection model while preserving data confidentiality. The proposed system promotes data protection, swift threat detection, and adaptable learning within distributed IoT networks. Experimental simulations have confirmed the model's efficacy, demonstrating its ability to accurately identify atypical device behavior, reduce false positives, and significantly decrease communication demands compared to conventional, centralized learning methods. The suggested strategy offers a robust, privacy-centric, and adaptable solution for securing IoT environments. It facilitates proactive responses to evolving digital threats and enhances trust in critical IoT applications across medical, industrial, and smart city environments.