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To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-processed using linear discriminant analysis methods to optimise the data, get integrated features, and ascertain the best projection. To assess system safety posture and find mapping relationships with network posture values, the cleaned data is subsequently input into an RBF neural network as training data. The reliability of the suggested technique for network security posture analysis is finally shown by simulations using the KDD Cup99 dataset and attack data from power information networks, with detection rates frequently surpassing 90%.

Security situational awareness of power information networks based on machine learning algorithms