Advances in edge computing are powering the development and deployment of
Internet of Things (IoT) systems in an effort to provide advanced services and
resource efficiency. However, large-scale IoT-based load-altering attacks
(LAAs) can have a serious impact on power grid operations such as destabilizing
the grid’s control loops. Timely detection and identification of any
compromised nodes is important to minimize the adverse effects of these attacks
on power grid operations. In this work, we present two data-driven algorithms
to detect and identify compromised nodes and the attack parameters of the LAAs.
The first, based on the Sparse Identification of Nonlinear Dynamics (SINDy)
approach, adopts a sparse regression framework to identify attack parameters
that best describes the observed dynamics. The second method, based on
physics-informed neural networks (PINN), adopts deep neural networks to infer
the attack parameters from the measurements. Both methods are presented
utilizing edge computing for deployment over decentralized architectures.
Extensive simulations performed on IEEE bus systems show that the proposed
algorithms outperform existing approaches, such as those based on unscented
Kalman filter, especially in systems that exhibit fast dynamics and are
effective in detecting and identifying locations of attack in a timely manner.

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