Enhanced Vulnerability Detection in SCADA Systems using Hyper-Parameter-Tuned Ensemble Learning

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.