  Advances in edge computing are powering the development and deployment of
Internet of Things (IoT) systems to provide advanced services and resource
efficiency. However, large-scale IoT-based load-altering attacks (LAAs) can
seriously impact power grid operations, such as destabilising the grid's
control loops. Timely detection and identification of any compromised nodes are
essential to minimise the adverse effects of these attacks on power grid
operations. In this work, two data-driven algorithms are proposed to detect and
identify compromised nodes and the attack parameters of the LAAs. The first
method, based on the Sparse Identification of Nonlinear Dynamics (SINDy)
approach, adopts a sparse regression framework to identify attack parameters
that best describe the observed dynamics. The second method, based on
physics-informed neural networks (PINN), employs neural networks to infer the
attack parameters from the measurements. Both algorithms are presented
utilising edge computing for deployment over decentralised architectures.
Extensive simulations are performed on IEEE 6-,14- and 39-bus systems to verify
the effectiveness of the proposed methods. Numerical results confirm that the
proposed algorithms outperform existing approaches, such as those based on
unscented Kalman filter, support vector machines (SVM), and neural networks
(NN), and effectively detect and identify locations of attack in a timely
manner.
