On Zero-Dynamics Stealthy Attacks with Learned State Space Models

Zero-dynamics stealthy attacks are a subset of false data injection attacks (FDIAs) that can be catastrophic as they are designed to be undetectable by traditional residual based anomaly detectors. The attacker's ability to successfully attack a system relies heavily on their capacity to learn...

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Bibliographic Details
Published in2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) pp. 1 - 6
Main Authors Harshbarger, Stephanie, Natarajan, Balasubramaniam, Vasserman, Eugene Y., Umar, Muhammad Farooq, Shadmand, Mohammad, Amariucai, George
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.01.2024
Subjects
Aerospace electronics
Analytical models
Computational modeling
Controllability
Detectors
Smart grids
System identification
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Summary:Zero-dynamics stealthy attacks are a subset of false data injection attacks (FDIAs) that can be catastrophic as they are designed to be undetectable by traditional residual based anomaly detectors. The attacker's ability to successfully attack a system relies heavily on their capacity to learn an accurate state space model. Utilizing a grey box approach to system identification, we show that even when the attacker is able to learn a state space model close enough to have a high probability of a successful attack, making small improvements to the system's anomaly detector causes the probability of a successful attack to drop drastically. Finally, we study the trade-offs between making the system less susceptible to zero-dynamics attacks and maintaining its controllability, by increasing the sampling time of the system, thus providing the attacker fewer samples to learn a state space model.
DOI:10.1109/SGRE59715.2024.10428796