The increasing deployment of Internet of Things (IoT) devices across diverse environments has introduced significant security challenges, particularly due to the distributed nature of IoT networks and the vast amount of sensitive data they generate. This research addresses the pressing issue of enhancing IoT security by proposing a decentralized and privacy-preserving approach that integrates federated learning models for intrusion detection. The proposed system leverages TensorFlow, Keras, and TensorFlow Federated libraries, implemented in the Python programming language, to train local models across multiple IoT clients. Each client learns from its own partition of the KDDCup 1999 dataset, a widely recognized benchmark in network intrusion detection. The system was evaluated across key performance metrics including accuracy, detection rate, and classification reliability. Experimental results demonstrated a consistent improvement in model accuracy from 93% and detection rate from 92% over 40 epochs. The distribution of detected attack types such as DDoS, phishing, malware, and ransomware further showcased the system’s practical applicability in heterogeneous IoT environments. This study confirms that federated learning is a viable approach to securing IoT systems, as it supports accurate and scalable threat detection while upholding data privacy. The model not only enhances trust and security but also demonstrates adaptability across various IoT scenarios with constrained computational resources. These scores clearly illustrated the advantage of the federated model in speed and responsiveness.
Keywords: federated learning, intrusion detection, iot security, privacy preservation, ransomware
