Blockchain-Enabled Trust Management for Federated Intrusion Detection in Edge-Based IoT Systems: A Systematic Review and Future Research Directions

Abstract

The rapid expansion of edge-enabled Internet of Things (IoT) infrastructures has improved real-time processing but introduced critical security vulnerabilities. Conventional intrusion detection systems (IDS) suffer from high communication overhead and privacy risks due to centralized data processing. This paper presents a comprehensive review of a blockchain-integrated, trust-aware Federated Intrusion Detection Framework (FIDS) designed for edge-IoT ecosystems. By utilizing federated learning, the system enables collaborative anomaly detection while preserving localized data privacy. To enhance reliability, we analyze dynamic trust evaluation mechanisms based on reputation scoring and blockchain verification to prevent model poisoning and adversarial attacks. The review addresses major research challenges, including lightweight model development for resource-constrained devices and the secure aggregation of local models. Finally, we provide a taxonomy of current methodologies and recommend future research directions for scalable, tamper-resistant architectures in critical edge-driven infrastructures.