论文标题
分析针对电网的改变负载攻击：一种罕见的事件采样方法
Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach
论文作者
论文摘要
通过操纵启用数万个启用的高战争电器（例如，WiFi控制的空调器），大规模的负载攻击（LAAS）可能会导致严重的破坏电网操作。在这项工作中，我们提出了一种罕见的事实抽样方法，以识别导致关键网络故障事件的LAA（由电网紧急响应（ER）的激活定义）。所提出的采样器旨在“跳过”在LAA实例上几乎没有兴趣（即那些没有触发网络故障的实例），从而大大降低了计算复杂性在识别影响力的LAA时。使用稀有事件采样器时，我们使用Kundur双面积系统（KTA）电源网络对LAA进行了广泛的模拟。结果有助于我们确定攻击者可以发起最有影响力的攻击的受害者节点，并提供有关LAAS空间分布如何触发ERS激活的见解。
By manipulating tens of thousands of internet-of-things (IoT) enabled high-wattage electrical appliances (e.g., WiFi-controlled air-conditioners), large-scale load-altering attacks (LAAs) can cause severe disruptions to power grid operations. In this work, we present a rare-event sampling approach to identify LAAs that lead to critical network failure events (defined by the activation of a power grid emergency response (ER)). The proposed sampler is designed to "skip" over LAA instances that are of little interest (i.e., those that do not trigger network failure), thus significantly reducing the computational complexity in identifying the impactful LAAs. We perform extensive simulations of LAAs using the Kundur two-area system (KTAS) power network while employing the rare-event sampler. The results help us identify the victim nodes from which the attacker can launch the most impactful attacks and provide insights into how the spatial distribution of LAAs triggers the activation of ERs.
