Security risk prediction technology for power monitoring system under the integration of OT and IT

Abstract

As an essential force for economic advancement and social stability, the security of the power system has always been a concern. Therefore, the security risks of power monitoring systems are a research focus. This study proposes a prediction method that integrates IT and OT for the security risk prediction of power monitoring systems. A basic indicator system for security risks for analyzing risk data is constructed, the support vector machine regression feature elimination method for predicting security risks in IT Technology is used. The experiment showed that the accuracy of the support vector machine regression feature elimination method was 92.35%, which was 6.06% higher than the error back propagation algorithm, 3.19% higher than the support vector machine algorithm, and 0.77% higher than the regression feature elimination algorithm. The maximum testing accuracy of the support vector machine regression feature elimination method was 0.96, which was 0.1 higher than the support vector machine algorithm, 0.04 higher than the regression feature elimination algorithm, and 0.17 higher than the back propagation algorithm. Therefore, the support vector machine regression feature elimination method can accurately predict power monitoring systems and has higher accuracy compared with other algorithms.

Keywords

• power monitoring system
• safety risk prediction
• support vector machine regression feature elimination method
• it technology
• ot technology