物联网技术下电网标识信息未授权窃取风险预测

doi:10.11930/j.issn.1004-9649.202311075

摘要/Abstract

摘要：

电网领域通常具有复杂的网络拓扑结构。为了降低电网安全风险，提出物联网技术下电网标识信息未授权窃取风险预测方法。通过物联网技术和漏洞扫描工具，量化电网标识信息未授权窃取的风险，并考虑不同漏洞的严重性，采用模糊层次分析法进行分险评估。研究结果表明：所提方法预测误差数值较小，设备认证强度高。

关键词: 物联网技术, 电网标识信息, 隐私泄露, 安全漏洞

Abstract:

The power grid field usually has a complex network topology. In order to reduce the power grid security risks, this paper proposes a risk prediction method unauthorized theft of power grid identity information under the Internet of Things technology. By using the Internet of Things technology and vulnerability scanning tools, the risk of unauthorized theft of grid identity information is quantified, and the severity of different vulnerabilities is considered, using the fuzzy hierarchical analysis method for risk assessment. The research results show that prediction error of the proposed method is small, and the device authentication strength is high.

Key words: IoT technology, grid identification information, privacy breach, security vulnerabilities

中图分类号:

TM726

吕华辉, 杨航, 林志达, 李慧娟, 付志博. 物联网技术下电网标识信息未授权窃取风险预测[J]. 中国电力, 2024, 57(12): 206-212.

Huahui LV, Hang YANG, Zhida LIN, Huijuan LI, Zhibo FU. Predicting the Risk of Unauthorized Theft of Grid Identification Information Under IoT Technology[J]. Electric Power, 2024, 57(12): 206-212.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311075

https://www.electricpower.com.cn/CN/Y2024/V57/I12/206

图/表 6

图 1 电网标识信息未授权窃取风险预测流程

Fig.1 Risk prediction flow chart of unauthorized theft of power grid identification information

表 1 采集得到的标识信息数据

Table 1 Data collected for identification information

信息编码	数据项名称	信息数据量/组
SJXXBM0917	IOS风险数据	240
SJXXBM0322	物理环境数据	354
SJXXBM0621	IP地址数据	168
SJXXBM0815	时间环境数据	157
SJXXBM0910	网络环境数据	259
SJXXBM0816	空间环境数据	375

图 2 搭建的物联网运行环境

Fig.2 Built IoT operating environment

图 3 标识信息窃取攻击监测结果

Fig.3 Marker information theft attack monitoring results

图 4 风险预测误差对比

Fig.4 Comparison of risk prediction errors

表 2 设备认证强度结果对比

Table 2 Comparison of equipment certification strength results 单位：%

实验次数	文献[7] 方法	文献[8] 方法	文献[9] 方法	设计的风险预测方法
10	86	80.0	62.0	95
20	90	75.0	67.0	92
30	89	76.0	61.0	95
40	91	77.9	60.8	94
50	90	78.0	70.0	92
60	89	78.0	71.0	92
70	81	82.0	68.0	91
80	83	81.0	63.0	94
90	87	80.0	61.0	93
100	92	79.0	64.0	95

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