URL : https://ieeexplore.ieee.org/abstract/document/11343792

Title : A Unified AI-PureChain Framework for Verifiable Intrusion Prevention in Industrial IoT Systems

Journal : IEEE Internet of Things Journal

DOI : 10.1109/JIOT.2026.3652250

Date : 12 January 2026

Abstract:

Securing industrial Internet of Things (IIoT) systems presents critical challenges due to resource constraints, expanding attack surfaces, and the inadequacy of conventional security solutions against sophisticated cyber threats. While AI-driven detection and blockchain technologies offer promise, existing frameworks suffer from computational inefficiency, a lack of real-time prevention, or insufficient auditability. This article introduces a unified AI-PureChain intrusion prevention framework that tightly integrates deep learning-based threat detection with an immutable PureChain ledger using proof of authority and association (PoA2) consensus. The proposed architecture achieves high-fidelity intrusion detection through hybrid CNN-BiLSTM models, attaining 99.76% accuracy on IoTForge Pro, 98.33% on WUSTL-IIoT-2021, and 98.11% on X-IIoTID datasets, while maintaining low inference latency (0.0016 s). The PureChain layer ensures tamper-proof audit trails with 24.56 transactions per second (TPS) throughput and 68-ms commit time, enabling verifiable prevention actions. Experimental results demonstrate complete attack mitigation (0% success rate) under high-traffic conditions while maintaining minimal resource consumption (14.49% CPU, 448-MB memory, 12.95-W power). This work represents a significant advancement in IIoT security by delivering a tightly coupled framework that simultaneously addresses detection accuracy, prevention reliability, and forensic accountability, thereby bridging critical gaps in current industrial security paradigms.