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<p>The Industrial Internet of Things (IIoT)continues to redefine industrial automation through connected smart devices, yet it remains highly vulnerable to privacy breaches and malicious intrusions. This research introduces ML-DCPP, a Machine Learning-based Dynamic Clustering and Privacy Preservation framework tailored to safeguard IIoT ecosystems. By integrating adaptive clustering via the LEACH protocol, secure key distribution through Public Key Generators (PKGs), and private data aggregation using the Location Privacy Tree (LPT) and Chinese Remainder Theorem (CRT), the framework addresses both communication efficiency and data confidentiality. To enhance security at the edge, differential privacy with Laplace noise is incorporated, and a Random Forest model is employed to detect malicious behavior with 99.89% accuracy, validated by a highly significant t-test (–7.87, p &lt; 0.0001). Experimental results reveal notable system improvements, including a reduction in latency to 0.45&#xa0;s, an increase in throughput to 280 tps, and an optimized cluster convergence time of 4.91&#xa0;s. ML-DCPP not only reinforces privacy and security measures but also demonstrates scalability and low overhead, making it a practical and forward-looking solution for securing industrial IoT infrastructures.</p>

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Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月8日

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  • Nabeela Hasan,
  • Saima Saleem,
  • Mudassir Khan,
  • Abdulatif Alabdultif,
  • Mohammad Mazhar Nezami,
  • Mansaf Alam

摘要

The Industrial Internet of Things (IIoT)continues to redefine industrial automation through connected smart devices, yet it remains highly vulnerable to privacy breaches and malicious intrusions. This research introduces ML-DCPP, a Machine Learning-based Dynamic Clustering and Privacy Preservation framework tailored to safeguard IIoT ecosystems. By integrating adaptive clustering via the LEACH protocol, secure key distribution through Public Key Generators (PKGs), and private data aggregation using the Location Privacy Tree (LPT) and Chinese Remainder Theorem (CRT), the framework addresses both communication efficiency and data confidentiality. To enhance security at the edge, differential privacy with Laplace noise is incorporated, and a Random Forest model is employed to detect malicious behavior with 99.89% accuracy, validated by a highly significant t-test (–7.87, p < 0.0001). Experimental results reveal notable system improvements, including a reduction in latency to 0.45 s, an increase in throughput to 280 tps, and an optimized cluster convergence time of 4.91 s. ML-DCPP not only reinforces privacy and security measures but also demonstrates scalability and low overhead, making it a practical and forward-looking solution for securing industrial IoT infrastructures.