  As one of the most sophisticated attacks against power grids, coordinated
cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure
and use a simultaneous cyber attack to mask its effect. This work proposes a
novel approach to detect such attacks and identify the location of the line
outages (due to the physical attack). The proposed approach consists of three
parts. Firstly, moving target defense (MTD) is applied to expose the physical
attack by actively perturbing transmission line reactance via distributed
flexible AC transmission system (D-FACTS) devices. MTD invalidates the
attackers' knowledge required to mask their physical attack. Secondly,
convolution neural networks (CNNs) are applied to localize line outage position
from the compromised measurements. Finally, model agnostic meta-learning (MAML)
is used to accelerate the training speed of CNN following the topology
reconfigurations (due to MTD) and reduce the data/retraining time requirements.
Simulations are carried out using IEEE test systems. The experimental results
demonstrate that the proposed approach can effectively localize line outages in
stealthy CCPAs.
