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Keywords

intelligent distribution transformer terminal unit, security situation awareness, SVM, RBF, random forest

Abstract

Due to its own vulnerabilities and the vulnerability of the communication network, the intelligent distribution transformer terminal deployed for the station area is vulnerable to network attacks. For solving the security problems existing in the intelligent distribution transformer terminal, this paper proposes an intelligent distribution transformer terminal network security situation awareness method based on RBF-SVM. Firstly, the potential network attack that the terminal may suffer is analyzed, the corresponding security detection indicators areextracted and normalized. Then, a nonlinear support vector machine (SVM) classifier based on the Gaussian (RBF) kernel function is conducted. The k-fold crossvalidation and grid search method is applied for determining the optimal parameters of C and g for the classifier, and the Security Situation Awareness model of the intelligent distribution transformer terminal is established. Finally, the test index data are substituted into the model for training and testing. The results show that compared with s random forest and logistic regression methods, the proposed method has a higher accuracy rate, can realize terminal security situation awareness, and canbe used for practical power terminal security protection.

DOI

10.19781/j.issn.1673-9140.2021.05.005

First Page

35

Last Page

40

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