论文标题
电网中的基于图的动态基于图的异常检测
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
论文作者
论文摘要
鉴于传感器随时间的读数从电网读取，我们如何才能准确检测出异常何时发生？实现此目标的关键部分是使用电网传感器网络实时快速检测到电网上发生任何异常事件（无论是自然的故障还是恶意）。该行业中现有的坏数据检测器缺乏强劲检测广泛类型的异常的复杂性，尤其是由于新兴网络攻击引起的异常情况，因为它们一次在一次网格的单个测量快照中运行。新的ML方法更广泛地适用，但通常不考虑拓扑变化对传感器测量的影响，因此无法适应历史数据中的常规拓扑调整。因此，我们提出了DynWatch是一种基于域知识和拓扑感知算法，用于使用放置在动态网格上的传感器进行异常检测。我们的方法在实验中是准确的，超过现有方法的20％或更多（F量）；快速，使用笔记本电脑在60k+分支机构上的平均每个传感器的平均每个传感器的平均运行量低于1.7ms，并在图形的大小上线性地缩放。
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose DYNWATCH, a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20% or more (F-measure) in experiments; and fast, running in less than 1.7ms on average per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly in the size of the graph.
