A Stealthier False Data Injection Attack against the Power Grid

Yan, Wei; Lou, Xin; Yau, David K. Y.; Yang, Ying; Saifuddin, Muhammad Ramadan Bin Mohamad; Wu, Jiyan; Winslett, Marianne

doi:10.1109/smartgridcomm51999.2021.9632337

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…for all areas. Furthermore, state noise and measurement noise are added to (12) according to zero-mean Gaussian distributions with a standard deviation of 0.03 Hz for frequency variables and √ 0.03 MW for power variables [19,56]. The above three factors (i.e., load fluctuation, state and measurement noise) are thus the main sources of randomness in our experiments.…”

Section: A Simulation Methodologymentioning

confidence: 99%

“…Notably, the proposed attack is able to bypass state-of-the-art bad data detection (BDD) methods used in power system state estimation. Another attack that can bypass BDD methods was proposed in [12]. In the first phase of the FDIA, the false measurements are designed to look like un-attacked cases, while the second phase finally drives the frequency beyond the safe range.…”

Section: Related Workmentioning

confidence: 99%

“…It is also worth to note that very few works [3,12] proposed attacks that can bypass bad data detection (BDD) techniques typically employed with power system state estimation. Nevertheless, these attacks are agnostic of any AGC-specific FDIA detectors, and should thus be detectable by those.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Reinforcement Learning Approach to Undetectable Attacks Against Automatic Generation Control

Shereen,

Kazari,

Dán

2024

IEEE Trans. Smart Grid

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Automatic generation control (AGC) is an essential functionality for ensuring the stability of power systems, and its secure operation is thus of utmost importance to power system operators. In this paper, we investigate the vulnerability of AGC to false data injection attacks that could remain undetected by traditional detection methods based on the area control error (ACE) and the recently proposed unknown input observer (UIO). We formulate the problem of computing undetectable attacks as a multi-objective partially observable Markov decision process. We propose a flexible reward function that allows to explore the trade-off between attack impact and detectability, and use the proximal policy optimization (PPO) algorithm for learning efficient attack policies. Through extensive simulations of a 3-area power system, we show that the proposed attacks can drive the frequency beyond critical limits, while remaining undetectable by state-of-the-art algorithms employed for fault and attack detection in AGC. Our results also show that detectors trained using supervised and unsupervised machine learning can both significantly outperform existing detectors.

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Section: A Simulation Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%