Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method

TL;DR

This paper proposes a novel FDIA detection method for power systems using advanced state prediction models like LSTM and GNN-LSTM, demonstrating high accuracy in simulations with noisy data.

Contribution

It introduces a new FDIA detection approach based on temporal and spatio-temporal state prediction models, enhancing detection accuracy and robustness in power systems.

Findings

State prediction models effectively identify FDIA events.

Proposed method maintains high detection accuracy across various scenarios.

Deployment strategies can reduce detection errors and computational load.

Abstract

With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system's stability like frequency stability, thereby leading to catastrophic failures. Therefore, an FDIA detection method would be valuable to protect power systems. FDIAs typically induce a discrepancy between the desired and the effective behavior of the power system dynamics. A suitable detection method can leverage power dynamics predictions to identify whether such a discrepancy was induced by an FDIA. This work investigates the efficacy of temporal and spatio-temporal state prediction models, such as Long Short-Term Memory (LSTM) and a combination of Graph Neural Networks (GNN) with LSTM, for predicting frequency dynamics in the absence of an FDIA but with noisy measurements, and thereby identify FDIA events. For demonstration purposes, the IEEE 39 New England Kron-reduced model simulated with a swing equation is considered. It is shown that the proposed state prediction models can be used as a building block for developing an effective FDIA detection method that can maintain high detection accuracy across various attack and deployment settings. It is also shown how the FDIA detection should be deployed to limit its exposure to detection inaccuracies and mitigate its computational burden.