基于RBF-SVM智能配变终端的网络安全态势评估
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TM77

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国家重点研发计划(2018YFB0904900,2018YFB0904903)


Research on network security situation awareness of intelligent distribution transformer terminal unit based on RBF-SVM
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    摘要:

    面向台区部署的智能配变终端受自身漏洞及通信网络脆弱性等多方面的影响,易受到网络攻击。针对智能配变终端存在的安全问题,提出一种基于 RBF-SVM 智能配变终端网络安全态势评估方法。首先,分析该终端可能遭受的网络攻击,提取相应的安全检测指标,并将检测指标数据归一化处理;然后构建基于高斯(RBF)核函数的非线性支持向量机(SVM)分类器,采用k 折交叉验证与网格搜索法确定该分类器的最优参数C 和g,建立智能配变终端安全态势评估模型;最后将检测指标数据样本代入模型中进行训练和测试。结果表明所提方法与随机森林和逻辑回归等方法相比较,具有更高的准确率,可实现终端安全态势评估,对电力终端安全防护具有一定的实用价值。

    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.

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引用本文

吴海涛,代尚林,乔中伟,等.基于RBF-SVM智能配变终端的网络安全态势评估[J].电力科学与技术学报,2021,36(5):35-40.
Wu Haitao, Dai Shanglin, Qiao Zhongwei, et al. Research on network security situation awareness of intelligent distribution transformer terminal unit based on RBF-SVM[J]. Journal of Electric Power Science and Technology,2021,36(5):35-40.

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  • 在线发布日期: 2021-11-16
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