A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids
This work addresses cyber attack detection for smart grids, which is a domain-specific problem, and appears incremental as it builds on existing GNN methods with temporal and residual components.
The paper tackles the problem of detecting and localizing false data injection and ramp attacks in smart grids by proposing a Temporal Graph Neural Network (TGNN) framework, which shows promising performance in simulations with evaluations of sensitivity to attack intensity and location and trade-offs between detection delay and accuracy.
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.