Article: Security issues and challenges in cloud of things-based applications for industrial automation Journal: International Journal of Grid and Utility Computing (IJGUC) 2026 Vol.17 No.1 pp.72 - 85 Abstract: To address security threats to cloud server serial port communication data, the research introduces a monitoring model that incorporates a passive clustering algorithm. The study uses a density-based approach to identify natural clusters in the data, allowing each cluster to have a different shape and size. The algorithm first evaluates the local densities of the data points and then assigns the data points to the nearest high-density areas based on these density values to form clusters. The results revealed an improvement of up to 0.06% in recall, up to 0.12% in accuracy and up to 0.09% in F1-score. The passive clustering algorithm improved on an average of 32.56% in F1-score, 24.78% in accuracy and 3.38% in recall compared to other methods. More advanced optimisations further improved the detection accuracy to 98.5678%, the false alarm rate to 1.4322% and the detection latency further reduced to 16,888.43 ms, highlighting the potential of the passive clustering algorithm in monitoring the security of serial port data for cloud server communication. As a result, the model constructed by the research can optimise the limitations existing in the traditional sub-cluster model, and then realise the service of user information data security, which has excellent practical value and broad application prospects. Inderscience Publishers - linking academia, business and industry through research

Title: Security issues and challenges in cloud of things-based applications for industrial automation

Authors: Cheng Liu; Yichao Zhang; Yanfeng Yu

Addresses: Department of Electronic Engineering, Zheng Zhou Railway Vocational & Technical College, Zhengzhou, China ' Students' Affairs Office, Zheng Zhou Railway Vocational & Technical College, Zhengzhou, China ' Students' Affairs Office, Zheng Zhou Railway Vocational & Technical College, Zhengzhou, China

Abstract: To address security threats to cloud server serial port communication data, the research introduces a monitoring model that incorporates a passive clustering algorithm. The study uses a density-based approach to identify natural clusters in the data, allowing each cluster to have a different shape and size. The algorithm first evaluates the local densities of the data points and then assigns the data points to the nearest high-density areas based on these density values to form clusters. The results revealed an improvement of up to 0.06% in recall, up to 0.12% in accuracy and up to 0.09% in F1-score. The passive clustering algorithm improved on an average of 32.56% in F1-score, 24.78% in accuracy and 3.38% in recall compared to other methods. More advanced optimisations further improved the detection accuracy to 98.5678%, the false alarm rate to 1.4322% and the detection latency further reduced to 16,888.43 ms, highlighting the potential of the passive clustering algorithm in monitoring the security of serial port data for cloud server communication. As a result, the model constructed by the research can optimise the limitations existing in the traditional sub-cluster model, and then realise the service of user information data security, which has excellent practical value and broad application prospects.

Keywords: passive clustering algorithm; cloud server; communication serial port; data security; security monitoring.

DOI: 10.1504/IJGUC.2026.150668

International Journal of Grid and Utility Computing, 2026 Vol.17 No.1, pp.72 - 85

Received: 25 Mar 2024
Accepted: 10 Mar 2025
Published online: 19 Dec 2025 *

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Title: Security issues and challenges in cloud of things-based applications for industrial automation

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