Integrated blockchain-federated learning framework for IOT security

Abstract

The rapid expansion of the Internet of Things (IoT) introduces complex, multi-layered security challenges that centralized security models and conventional intrusion detection systems struggle to address. This paper presents a multi-layer security framework that integrates Blockchain for decentralized trust management, Federated Learning (FL) for privacy-preserving intelligence, and Generative Adversarial Networks (GANs) for class-imbalance mitigation, with each technique mapped to a specific IoT architectural layer. The framework is evaluated on the NSL-KDD intrusion detection dataset, using GAN-based augmentation to improve detection of rare attack classes, and is further validated through case studies in healthcare and smart-agriculture deployments. The proposed GAN-FL-Blockchain model achieves 95% detection accuracy, 94% precision, 96% recall, and a 94.5% F1-score, improving detection rate by 13% and reducing latency by nearly 50% (150 ms vs. 300 ms) relative to a SMOTE+SVM baseline. These results demonstrate a scalable, regulation-compliant approach to layered IoT security. Breach identification, ML framework development, and integration of individual modules in a seamless and synchronized manner. Tailored countermeasures, ML integration, experimental validation with the auto encoder-based anomaly detection, integration of Graph Neural Network with the Generative Adversarial Network and Federated Learning procedure. Enhanced breach detection, practical implementation guidance with the validation accuracy value of 98%. The integrated model will be targeted to have overall latency minimization in the future.

The authors' abstract, as published at the source. Journal of Cloud Computing Advances Systems and Applications, 2026 · DOI ↗