# An Online Detection Framework for Cyber Attacks on Automatic Generation Control

arXiv: https://arxiv.org/abs/1712.06417 · HTML: https://mlsanity.com/paper/1712.06417/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2017.

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

- [A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2310.06948/) (2023)
- [Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities](https://mlsanity.com/paper/2504.09363/) (2025)
- [Glass-Box Deep Learning for FDIA Detection in Nonlinear Automatic Generation Control: A Kolmogorov-Arnold Network Approach](https://mlsanity.com/paper/2509.05259/) (2025)

## Abstract (arXiv)

We propose an online framework to detect cyber attacks on Automatic Generation Control (AGC). A cyber at- tack detection algorithm is designed based on the approach of Dynamic Watermarking. The detection algorithm provides a theoretical guarantee of detection of cyber attacks launched by sophisticated attackers possessing extensive knowledge of the physical and statistical models of targeted power systems. The proposed framework is practically implementable, as it needs no hardware update on generation units. The efficacy of the proposed framework is validated in both four-area system and 140-bus system.