Biblio

The growth of inter-dependency intricacies of Supervisory Control and Data Acquisition (SCADA) systems in industrial operations generates a likelihood of increased vulnerability to malicious threats and machine learning approaches have been extensively utilized in the research for vulnerability detection. Nonetheless, to improve security, an enhanced vulnerability detection using hyper-parameter-tune machine learning is proposed for early detection, classification and mitigation of SCADA communication and transmission networks by classifying benign, or malicious DNS attacks. The proposed scheme, an ensemble optimizer (GentleBoost) upon hyper-parameter tuning, gave a comparative achievement. From the simulation results, the proposed scheme had an outstanding performance within the shortest possible time with an accuracy of 99.49%, 99.23% for precision, and a recall rate of 99.75%. Also, the model was compared to other contemporary algorithms and outperformed all the other algorithms proving to be an approach to keep abreast of the SCADA network vulnerabilities and attacks.

This study has built a simulation of a smart home system by the Alibaba ECS. The architecture of hardware was based on edge computing technology. The whole method would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.