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A novel false data injection attack (FDIA) model against DC state estimation is proposed, which requires no network parameters and exploits only limited phasor measurement unit (PMU) data. The proposed FDIA model can target specific states and launch large deviation attacks using estimated line parameters. Sufficient conditions for the proposed method are also presented. Different attack vectors are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection (BDD) with high success rates of up to 95.3%.
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
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to in
An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization probl
In this chapter we review some of the basic attack constructions that exploit a stochastic description of the state variables. We pose the state estimation problem in a Bayesian setting and cast the bad data detection procedure as a Bayesian hypothes
The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper demonstrates that the observable interaction process of MRNs under formation control will present increasingly severe threats. Specifically,