  Load-altering attacks targetting a large number of IoT-based high-wattage
devices (e.g., smart electric vehicle charging stations) can lead to serious
disruptions of power grid operations. In this work, we aim to uncover
spatiotemporal characteristics of LAAs that can lead to serious impact. The
problem is challenging since existing protection measures such as $N-1$
security ensures that the power grid is naturally resilient to load changes.
Thus, strategically injected load perturbations that lead to network failure
can be regarded as \emph{rare events}. To this end, we adopt a rare-event
sampling approach to uncover LAAs distributed temporally and spatially across
the power network. The key advantage of this sampling method is the ability of
sampling efficiently from multi-modal conditional distributions with
disconnected support. Furthermore, we systematically compare the impacts of
static (one-time manipulation of demand) and dynamic (attack over multiple time
periods) LAAs. We perform extensive simulations using benchmark IEEE test
simulations. The results show (i) the superiority and the need for rare-event
sampling in the context of uncovering LAAs as compared to other sampling
methodologies, (ii) statistical analysis of attack characteristics and impacts
of static and dynamic LAAs, and (iii) cascade sizes (due to LAA) for different
network sizes and load conditions.
