论文标题
部分可观测时空混沌系统的无模型预测
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids
论文作者
论文摘要
本文提出了一个时间图神经网络（TGNN）框架，用于检测和定位错误的数据注入和对智能电网中系统状态的坡道攻击。通过GNN框架捕获系统的拓扑信息以及状态测量可以提高检测机制的性能。该问题通过GNN和消息传递机制以识别异常测量结果而被称为分类问题。消息传递的聚合过程中使用的残留块和封闭的复发单元可以改善计算时间和性能。已通过对电力系统状态的广泛模拟进行了评估，并显示出有希望的性能。该模型对攻击的强度和位置的敏感性以及模型的检测延迟与检测精度的敏感性也得到了评估。
This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the GNN framework along with the state measurements can improve the performance of the detection mechanism. The problem is formulated as a classification problem through a GNN with message passing mechanism to identify abnormal measurements. The residual block used in the aggregation process of message passing and the gated recurrent unit can lead to improved computational time and performance. The performance of the proposed model has been evaluated through extensive simulations of power system states and attack scenarios showing promising performance. The sensitivity of the model to intensity and location of the attacks and model's detection delay versus detection accuracy have also been evaluated.
