Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things
Abstract 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 Prese...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Computing |
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| Online Access: | https://doi.org/10.1007/s10791-025-09689-w |
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| author | Nabeela Hasan Saima Saleem Mudassir Khan Abdulatif Alabdultif Mohammad Mazhar Nezami Mansaf Alam |
| author_facet | Nabeela Hasan Saima Saleem Mudassir Khan Abdulatif Alabdultif Mohammad Mazhar Nezami Mansaf Alam |
| author_sort | Nabeela Hasan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d564e8fcae4a483cad2c140a65736e02 |
| institution | Kabale University |
| issn | 2948-2992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Computing |
| spelling | doaj-art-d564e8fcae4a483cad2c140a65736e022025-08-20T03:46:13ZengSpringerDiscover Computing2948-29922025-08-0128112710.1007/s10791-025-09689-wMachine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of thingsNabeela Hasan0Saima Saleem1Mudassir Khan2Abdulatif Alabdultif3Mohammad Mazhar Nezami4Mansaf Alam5Artififcial Intelligence and Machine Learning Department, New Delhi Institute of ManagementInstitute of Information Technology and Management, GGSIPUDepartment of Computer Science, College of Computer Science, Applied College Tanumah, King Khalid UniversityDepartment of Computer Science, College of Computer, Qassim UniversityDepartment of Computer Science and Artificial Intelligence, College of Computer Science and Information Technology, University of BishaDepartment of Computer Science, Jamia Millia IslamiaAbstract 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.https://doi.org/10.1007/s10791-025-09689-wCryptographyDynamic clusteringIndustrial internet of things (IIoT)Machine learningMalicious node detectionPrivacy preservation |
| spellingShingle | Nabeela Hasan Saima Saleem Mudassir Khan Abdulatif Alabdultif Mohammad Mazhar Nezami Mansaf Alam Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things Discover Computing Cryptography Dynamic clustering Industrial internet of things (IIoT) Machine learning Malicious node detection Privacy preservation |
| title | Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things |
| title_full | Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things |
| title_fullStr | Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things |
| title_full_unstemmed | Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things |
| title_short | Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things |
| title_sort | machine learning enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things |
| topic | Cryptography Dynamic clustering Industrial internet of things (IIoT) Machine learning Malicious node detection Privacy preservation |
| url | https://doi.org/10.1007/s10791-025-09689-w |
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