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

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.