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.

翻译：针对大量基于IoT的高功率设备（例如智能电动汽车充电站）的负荷篡改攻击可能导致电网运行的严重中断。本文旨在揭示能够造成严重影响的LAAs的时空特性。由于现有保护措施（如N-1安全准则）确保电网对负荷变化具有天然弹性，该问题具有挑战性。因此，导致网络故障的策略性注入负荷扰动可被视为稀有事件。为此，我们采用稀有事件采样方法来揭示在电网时空分布的LAAs。该采样方法的关键优势在于能够高效地从具有不连通支撑的多模态条件分布中进行采样。此外，我们系统比较了静态LAA（一次性操控需求）与动态LAA（多时段攻击）的影响。使用IEEE基准测试仿真进行了广泛实验。结果表明：（i）相比其他采样方法，稀有事件采样在揭示LAAs方面具有优越性和必要性；（ii）对攻击特性及静态/动态LAA影响的统计分析；（iii）不同网络规模与负荷条件下（由LAA导致的）的级联规模。