  As the complexities of Dynamic Data Driven Applications Systems increase,
preserving their resilience becomes more challenging. For instance, maintaining
power grid resilience is becoming increasingly complicated due to the growing
number of stochastic variables (such as renewable outputs) and extreme weather
events that add uncertainty to the grid. Current optimization methods have
struggled to accommodate this rise in complexity. This has fueled the growing
interest in data-driven methods used to operate the grid, leading to more
vulnerability to cyberattacks. One such disruption that is commonly discussed
is the adversarial disruption, where the intruder attempts to add a small
perturbation to input data in order to "manipulate" the system operation.
During the last few years, work on adversarial training and disruptions on the
power system has gained popularity. In this paper, we will first review these
applications, specifically on the most common types of adversarial disruptions:
evasion and poisoning disruptions. Through this review, we highlight the gap
between poisoning and evasion research when applied to the power grid. This is
due to the underlying assumption that model training is secure, leading to
evasion disruptions being the primary type of studied disruption. Finally, we
will examine the impacts of data poisoning interventions and showcase how they
can endanger power grid resilience.
