Open Access

ARTICLE

MBID: A Scalable Multi-Tier Blockchain Architecture with Physics-Informed Neural Networks for Intrusion Detection in Large-Scale IoT Networks

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal², Heba G. Mohamed³, Muhammad Sheraz⁴, Teong Chee Chuah^4,*

1 School of Software, Northwestern Polytechnical University, Xi’an, 710072, China
2 School of Electronic and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China
3 Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, 63100, Selangor, Malaysia

* Corresponding Authors: Junsheng Wu. Email: ; Teong Chee Chuah. Email:

(This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)

Computer Modeling in Engineering & Sciences 2025, 144(2), 2647-2681. https://doi.org/10.32604/cmes.2025.068849

Received 08 June 2025; Accepted 22 July 2025; Issue published 31 August 2025

Abstract

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.

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Keywords

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