An Efficient Data-Driven False Data Injection Attack in Smart Grids
Paper i proceeding, 2018
Data-driven false data injection attack is one of the
emerging techniques in smart grids, provided that the adversary
can monitor the meter readings. The basic idea is constructing attack
vectors from the estimated signal subspace, without knowing
the system measurement matrix. However, its stealthy performance is
significantly influenced by the accuracy of the estimated subspace.
Furthermore, it is computationally demanding, because full-size
singular value decomposition (SVD) is required for model order
selection. In this paper, we propose a truncated SVD based
computationally efficient attacking scheme using only the first
dominant eigenvector. Both experiment and simulation results are
provided to evaluate the performance of the proposed scheme.
Compared with the standard false data injection techniques
with known measurement matrix, similar stealthy performance
is achieved with a reasonable computational complexity.