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

Abstract

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%.

KEYWORDS:

• Cyber security situational awareness
• power information network
• cyber-attack
• linear discriminant analysis (LDA)
• RBF

Acknowledgments

The authors would like to show sincere thanks to those techniques who have contributed to this research.

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Chao Wang; data collection: Jia-han Dong, Guang-xin GUO; analysis and interpretation of results: Tian-yu REN, Xiao-hu WANG; draft manuscript preparation: Ming-yu Pan. All authors reviewed the results and approved the final version of the manuscript.

Consent for publication

All authors reviewed the results, approved the final version of the manuscript, and agreed to publish it.

Data availability

The experimental data used to support the findings of this study are available from the corresponding author upon request.

Disclosure statement

No potential conflict of interest was reported by the author(s).