Trust evaluation model of power terminal based on equipment portrait

IF 1.9 Q4 ENERGY & FUELS Global Energy Interconnection Pub Date : 2023-12-01 DOI:10.1016/j.gloei.2023.11.009
Erxia Li , Zilong Han , Chaoqun Kang , Tao Yu , Yupeng Huang
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Abstract

As the number of power terminals continues to increase and their usage becomes more widespread, the security of power systems is under great threat. In response to the lack of effective trust evaluation methods for terminals, we propose a trust evaluation model based on equipment portraits for power terminals. First, we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic. Second, we propose an exception evaluation method based on syntax and semantics. The key fields of each message are extracted, and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal. Thus, by combining the network flow order, syntax, and semantic analysis, an equipment portrait can be constructed to guarantee security of the power network terminals. We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time. Finally, the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate, as well as a higher real-time performance, which is more suitable for power terminals.

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基于设备肖像的电力终端信任评估模型
随着电力终端数量的不断增加和使用的日益广泛,电力系统的安全性受到了极大的威胁。针对目前缺乏有效的终端信任评估方法的现状,我们提出了一种基于设备画像的电力终端信任评估模型。首先,我们提出了一种基于网络流序的异常评估方法,通过监测网络流量的外部特征来评估异常终端。其次,我们提出了基于语法和语义的异常评估方法。提取每条报文的关键字段,统计分析报文中关键词的出现频率,得出评估终端状态的关键词频率和时隙阈值。这样,结合网络流序、语法和语义分析,就可以构建设备画像,保证电力网络终端的安全。然后,我们提出了一种基于设备画像的信任评估方法,实时计算信任值。最后,终端异常检测的实验结果表明,所提出的模型具有更高的检测率和更低的误检率,以及更高的实时性,更适用于电力终端。
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来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
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