  The false data injection (FDI) attack cannot be detected by the traditional
anomaly detection techniques used in the energy system state estimators. In
this paper, we demonstrate how FDI attacks can be constructed blindly, i.e.,
without system knowledge, including topological connectivity and line reactance
information. Our analysis reveals that existing FDI attacks become detectable
(consequently unsuccessful) by the state estimator if the data contains grossly
corrupted measurements such as device malfunction and communication errors. The
proposed sparse optimization based stealthy attacks construction strategy
overcomes this limitation by separating the gross errors from the measurement
matrix. Extensive theoretical modeling and experimental evaluation show that
the proposed technique performs more stealthily (has less relative error) and
efficiently (fast enough to maintain time requirement) compared to other
methods on IEEE benchmark test systems.
