# Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

arXiv: https://arxiv.org/abs/2111.06346 · HTML: https://mlsanity.com/paper/2111.06346/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning](https://mlsanity.com/paper/2312.10587/) (2023)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids](https://mlsanity.com/paper/2606.25200/) (2026)

## Abstract (arXiv)

Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the key challenges for MTD in power grid is to design its real-time implementation with performance guarantees against unknown attacks. Converting from the noiseless assumptions in the literature, this paper investigates the MTD design problem in a noisy environment and proposes, for the first time, the concept of robust MTD to guarantee the worst-case detection rate against all unknown attacks. We theoretically prove that, for any given MTD strategy, the minimal principal angle between the Jacobian subspaces corresponds to the worst-case performance against all potential attacks. Based on this finding, robust MTD algorithms are formulated for the systems with both complete and incomplete configurations. Extensive simulations using standard IEEE benchmark systems demonstrate the improved average and worst-case performances of the proposed robust MTD against state-of-the-art algorithms. All codes are available at https://github.com/xuwkk/Robust_MTD.