A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids

TL;DR

This paper introduces a Temporal Graph Neural Network framework that enhances detection and localization of cyber attacks in smart grids by leveraging system topology and state measurements, demonstrating promising simulation results.

Contribution

The paper proposes a novel TGNN model with residual blocks and gated recurrent units for improved attack detection and localization in smart grids.

Findings

Effective detection of false data injection and ramp attacks.

High accuracy in attack localization.

Robustness to attack intensity and location variations.

Abstract

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.