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<p>The growing interconnectivity of industrial systems has intensified the need for secure, intelligent, and scalable data transfer mechanisms within Industrial Internet of Things (IIoT) environments. Despite rapid IIoT adoption, industrial data transfer remains vulnerable to high-volume, dynamic cyber anomalies and consensus-level attacks, while existing security mechanisms struggle to jointly deliver low-latency, scalable, and trustworthy communication under large-scale adversarial deployments. This study introduces a Secure Dual-Consensus Blockchain-Enabled Deep Learning Framework (SD-BDL) that unifies blockchain security and adaptive anomaly detection to ensure trustworthy and efficient IIoT communication. The framework employs a hybrid consensus mechanism, integrating Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve enhanced fault tolerance, reduced latency, and protection against collusion and Sybil attacks. To address the dynamic and high-volume nature of IIoT data streams, a CNN–LSTM model is deployed for real-time anomaly detection, with hyperparameters optimized using the Adaptive Aquila Optimization (AAO) algorithm—identified as the most effective technique for achieving rapid convergence, high detection accuracy, and balanced exploration–exploitation. The proposed SD-BDL framework is evaluated on an IIoT dataset, incorporating preprocessing steps to mitigate class imbalance, missing values, and noise interference. Experimental outcomes demonstrate a significant improvement in performance metrics, achieving an R² score of 0.985, throughput enhancement of 25.2%, and latency reduction of 19.4% compared with benchmark models using PSO, GA, and Bayesian optimization. The hybrid consensus blockchain further ensures transaction integrity, tamper resistance, and low-energy overhead, validating its robustness under adversarial and large-scale deployment scenarios involving over 1,000 nodes. This research contributes a novel, energy-efficient, and scalable architecture for industrial data protection, setting a foundation for future integration with 6G-enabled IIoT systems, federated trust networks, and lightweight transformer-based threat detection frameworks.</p>

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Secure dual-consensus blockchain and adaptive deep learning framework for intelligent data transfer in industrial IoT environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Alaa A. Qaffas

摘要

The growing interconnectivity of industrial systems has intensified the need for secure, intelligent, and scalable data transfer mechanisms within Industrial Internet of Things (IIoT) environments. Despite rapid IIoT adoption, industrial data transfer remains vulnerable to high-volume, dynamic cyber anomalies and consensus-level attacks, while existing security mechanisms struggle to jointly deliver low-latency, scalable, and trustworthy communication under large-scale adversarial deployments. This study introduces a Secure Dual-Consensus Blockchain-Enabled Deep Learning Framework (SD-BDL) that unifies blockchain security and adaptive anomaly detection to ensure trustworthy and efficient IIoT communication. The framework employs a hybrid consensus mechanism, integrating Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve enhanced fault tolerance, reduced latency, and protection against collusion and Sybil attacks. To address the dynamic and high-volume nature of IIoT data streams, a CNN–LSTM model is deployed for real-time anomaly detection, with hyperparameters optimized using the Adaptive Aquila Optimization (AAO) algorithm—identified as the most effective technique for achieving rapid convergence, high detection accuracy, and balanced exploration–exploitation. The proposed SD-BDL framework is evaluated on an IIoT dataset, incorporating preprocessing steps to mitigate class imbalance, missing values, and noise interference. Experimental outcomes demonstrate a significant improvement in performance metrics, achieving an R² score of 0.985, throughput enhancement of 25.2%, and latency reduction of 19.4% compared with benchmark models using PSO, GA, and Bayesian optimization. The hybrid consensus blockchain further ensures transaction integrity, tamper resistance, and low-energy overhead, validating its robustness under adversarial and large-scale deployment scenarios involving over 1,000 nodes. This research contributes a novel, energy-efficient, and scalable architecture for industrial data protection, setting a foundation for future integration with 6G-enabled IIoT systems, federated trust networks, and lightweight transformer-based threat detection frameworks.