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Targeted False Data Injection Attacks Against AC State Estimation Without Network Parameters
DOI:10.1109/TSG.2021.3106246.png)
摘要
En 中文
State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system. Relying heavily on meter measurements, state estimation has proven to be vulnerable to cyber attacks. In this paper, a novel targeted false data injection attack (FDIA) model against AC state estimation is proposed. Leveraging on the intrinsic load dynamics in ambient conditions and important properties of the Ornstein-Uhlenbeck process, we, from the viewpoint of intruders, design an algorithm to extract power network parameters purely from PMU data, which are further used to construct the FDIA vector. Requiring no network parameters and relying only on limited phasor measurement unit (PMU) data, the proposed FDIA model can target specific states and launch large deviation attacks. Sufficient conditions for the proposed FDIA model are also developed. Various attack vectors and attacking regions are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection and launch targeted large deviation attacks with very high probabilities.
Keyword:
State estimation
Phasor measurement units
Power measurement
Voltage measurement
Meters
Data models
Topology
AC state estimation
false data injection attacks
network-independent
Ornstein-Uhlenbeck process
phasor measurement unit
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
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Online Measurement-Based Estimation of Dynamic System State Matrix in Ambient Conditions环境条件下基于在线测量的动态系统状态矩阵估计
Network Parameter Coordinated False Data Injection Attacks Against Power System AC State Estimation针对电力系统交流状态估计的网络参数协调虚假数据注入攻击

