TY  - EJOU
AU  - Ullah, Saeed 
AU  - Wu, Junsheng 
AU  - Kamal, Mian Muhammad 
AU  - Mohamed, Heba G. 
AU  - Sheraz, Muhammad 
AU  - Chuah, Teong Chee 

TI  - MBID: A Scalable Multi-Tier Blockchain Architecture with Physics-Informed Neural Networks for Intrusion Detection in Large-Scale IoT Networks
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 2
SN  - 1526-1506

AB  - The Internet of Things (IoT) ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027, operating in distributed networks with resource limitations and diverse system architectures. The current conventional intrusion detection systems (IDS) face scalability problems and trust-related issues, but blockchain-based solutions face limitations because of their low transaction throughput (Bitcoin: 7 TPS (Transactions Per Second), Ethereum: 15–30 TPS) and high latency. The research introduces MBID (Multi-Tier Blockchain Intrusion Detection) as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection, which solves the problems in huge IoT networks. The MBID system uses a four-tier architecture that includes device, edge, fog, and cloud layers with blockchain implementations and Physics-Informed Neural Networks (PINNs) for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority (HDPoA) and Delegated Proof of Stake (DPoS). The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System (IPFS) integration. Experimental evaluations demonstrate exceptional performance, achieving a detection accuracy of 99.84%, an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%, and a near-instantaneous edge detection latency of 0.40 ms. The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration, providing a clear, evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput. The proposed architecture represents a significant advancement in blockchain-based security for IoT networks, effectively balancing the trade-offs between scalability, security, and decentralization.
KW  - Internet of things; blockchain; intrusion detection; physics-informed neural networks; scalability; security

DO  - 10.32604/cmes.2025.068849