# Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid

arXiv: https://arxiv.org/abs/1803.00128 · HTML: https://mlsanity.com/paper/1803.00128/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks](https://mlsanity.com/paper/2112.13166/) (2021)
- [A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems](https://mlsanity.com/paper/2210.06729/) (2022)
- [Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence](https://mlsanity.com/paper/2405.02372/) (2024)

## Abstract (arXiv)

For a safe and reliable operation of the smart grid, timely detection of cyber-attacks is of critical importance. Moreover, considering smarter and more capable attackers, robust detection mechanisms are needed against a diverse range of cyber-attacks. With these purposes, we propose a robust online detection algorithm for (possibly combined) false data injection (FDI) and jamming attacks, that also provides online estimates of the unknown and time-varying attack parameters and recovered state estimates. Further, considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm, we propose additional countermeasures. Numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyber-attacks.