Federated Multi-Zone Blockchain for Decentralized Intrusion Detection in IoT

Abstract

As IoT networks continue to grow at a rapid pace, there's an increasing need for security models that can stay lightweight without sacrificing resilience. Most existing blockchain based intrusion detection systems make two problematic assumptions: they treat trust as static, and they rely on centralized data collection. Both of these clashes with the practical constraints of IoT environments and create exploitable vulnerabilities. In this paper, we propose a Federated Multi-Zone DAG Blockchain that brings together federated learning and Zero Trust principles. The key idea is to train intrusion detection models locally either at the IoT device level or at fog nodes so that only model updates get shared across the network, not the raw data itself. These updates are then recorded and verified in the blockchain using a lightweight Proof-of-Authentication consensus. This means no node ever gets permanent trusted status, instead, all actions undergo continuous validation. This federated, Zero Trust approach has several benefits. It keeps data private, cuts down on communication costs, and can adapt on the fly when nodes get compromised. By building both the learning and verification processes directly into the multi-zone DAG structure, our framework manages to improve privacy, scalability, and energy efficiency all at once. The result is a decentralized defense system that can keep up with the evolving threat landscape in IoT environments.