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

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.