基于FP-Growth算法的新能源配电网CPS网络攻击检测方法

李瑞; 刘珊; 闫磊

doi:10.11959/j.issn.1000-0801.2024229

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基于FP-Growth算法的新能源配电网CPS网络攻击检测方法

研究与开发 | 更新时间：2025-03-17

- 基于FP-Growth算法的新能源配电网CPS网络攻击检测方法
- Network attack detection method for CPS of active distribution network with renewable energy based on FP-Growth algorithm
- 电信科学 2024年40卷第11期页码：103-113
- 作者机构：
  
  1.国网山西省电力公司电力科学研究院，山西太原 030001
  2.国网山西省电力公司，山西太原 030021
- 作者简介：
  
  [ "李瑞（1981- ），男，博士，国网山西省电力公司电力科学研究院正高级工程师，主要研究方向为电力系统自动化及其智能数字化技术。" ]
  刘珊（1987- ），女，国网山西省电力公司电力科学研究院高级工程师，主要研究方向为电网数字化及网络攻防技术。
  闫磊（1985- ），男，国网山西省电力公司高级工程师，主要研究方向为电网调度自动化及通信。
- 基金信息：
  
  国网山西省电力公司科技项目(520530230009)
- DOI：10.11959/j.issn.1000-0801.2024229
  中图分类号： TM73;TP181
- 收稿日期：2024-06-07，
  
  修回日期：2024-10-15，
  
  纸质出版日期：2024-11-20
- 稿件说明：
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李瑞,刘珊,闫磊.基于FP-Growth算法的新能源配电网CPS网络攻击检测方法[J].电信科学,2024,40(11):103-113.

LI Rui,LIU Shan,YAN Lei.Network attack detection method for CPS of active distribution network with renewable energy based on FP-Growth algorithm[J].Telecommunications Science,2024,40(11):103-113.
李瑞,刘珊,闫磊.基于FP-Growth算法的新能源配电网CPS网络攻击检测方法[J].电信科学,2024,40(11):103-113. DOI： 10.11959/j.issn.1000-0801.2024229.

LI Rui,LIU Shan,YAN Lei.Network attack detection method for CPS of active distribution network with renewable energy based on FP-Growth algorithm[J].Telecommunications Science,2024,40(11):103-113. DOI： 10.11959/j.issn.1000-0801.2024229.

摘要

为有效分析识别有源配电网信息物理系统（cyber physical system，CPS）状态，提出基于FP-Growth算法的有源配电网信息物理系统网络攻击检测方法。首先分析考虑网络攻击的有源配电网控制模型及CPS网络攻击影响机理，通过实时仿真平台对有源配电网CPS信息侧和物理侧进行监测来获取原始数据；然后通过额定电压、电流值制订数据离散化规则，并根据规则对原始数据进行离散量化处理来生成事件序列。在此基础上，采用FP-Growth算法挖掘历史数据异常信号的频繁项集和强关联关系，通过已有频繁序列特征对新的攻击类别和故障点进行识别，实现对有源配电网CPS网络攻击的检测。最后，仿真实验验证了所提方法的可行性和有效性。

Abstract

In order to effectively analyze and identify the cyber physical system (CPS) status of active distribution network

a network attack detection method based on FP-Growth algorithm for active distribution network cyber physical system was proposed. Firstly

the active distribution network control model considering network attack and the impact mechanism of CPS network attack were analyzed

and the raw data was obtained by monitoring the CPS information side and physical side of the active distribution network through the real-time simulation platform. Then

the data discretization rules were formulated through the rated voltage and current values

and the original data were discretized and quantized according to the rules to generate event sequences. On this basis

the FP-Growth algorithm was used to mine the frequent items and strong correlations of abnormal signals in historical data

and new attack categories and fault points were identified through the existing frequent sequence features. The detection of CPS network attack on active distribution network was realized. Finally

the feasibility and effectiveness of the proposed method were verified by a simulation experiment.

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references

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