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面向互动需求响应的虚假数据注入攻击及其检测方法
作者:
作者单位:

新能源电力系统国家重点实验室(华北电力大学),北京市 102206

摘要:

在互动需求响应(IDR)框架下,对需求侧虚假数据注入(FDI)攻击的脆弱性进行分析,凝练了对用户能量管理系统(UEMS)的攻击过程。首先,采用主从博弈建立了针对IDR的FDI攻击模型,并证明其斯塔克伯格均衡存在且唯一。提出了一种基于分布式事件触发机制与机器学习的攻击检测方法,事件触发机制在UEMS中执行,系统检测在能量零售商(ER)中基于长短期记忆网络(LSTM)进行。最后,通过实例分析验证了所提方法的有效性。

关键词:

基金项目:

国家自然科学基金资助项目(51877076)。

通信作者:

作者简介:

陈刘东(1997—),男,硕士研究生,主要研究方向:信息物理系统、配电网优化。E-mail:liudong@ncepu.edu.cn
刘念(1981—),男,通信作者,博士,教授,博士生导师,主要研究方向:智能配用电、综合能源系统、信息物理系统。E-mail:nianliu@ncepu.edu.cn


False Data Injection Attack and Its Detection Method for Interactive Demand Response
Author:
Affiliation:

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electrical Power University), Beijing 102206, China

Abstract:

The vulnerability to the demand-side false data injection (FDI) under the framework of interactive demand response (IDR) is analyzed. The process of attack on the user energy management system (UEMS) is also summarized. First, the Stackelberg game is used to establish an FDI attack model against IDR and verify the existence and uniqueness of Stackelberg equilibrium. The paper proposes an attack detection method based on the distributed event-triggered mechanism and machine learning. The event-triggered mechanism is implemented in UEMS. The system detection is carried out in energy retailers (ERs) based on the long short-term memory network (LSTM). Finally, the effectiveness of the proposed method is verified through case analysis.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 51877076).
引用本文
[1]陈刘东,刘念.面向互动需求响应的虚假数据注入攻击及其检测方法[J].电力系统自动化,2021,45(3):15-23. DOI:10.7500/AEPS20200505006.
CHEN Liudong, LIU Nian. False Data Injection Attack and Its Detection Method for Interactive Demand Response[J]. Automation of Electric Power Systems, 2021, 45(3):15-23. DOI:10.7500/AEPS20200505006.
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  • 收稿日期:2020-05-05
  • 最后修改日期:2020-07-27
  • 录用日期:
  • 在线发布日期: 2021-02-03
  • 出版日期:
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