@inproceedings{97ad4dd9c97e4f51b52ba73ab1dc9a58,
title = "Learning Requirements for Stealth Attacks",
abstract = "The learning data requirements are analyzed for the construction of stealth attacks in state estimation. In particular, the training data set is used to compute a sample covariance matrix that results in a random matrix with a Wishart distribution. The ergodic attack performance is defined as the average attack performance obtained by taking the expectation with respect to the distribution of the training data set. The impact of the training data size on the ergodic attack performance is characterized by proposing an upper bound for the performance. Simulations on the IEEE 30-Bus test system show that the proposed bound is tight in practical settings.",
keywords = "data injection attacks, information theory, random matrix theory, stealth attacks",
author = "Ke Sun and Inaki Esnaola and Tulino, {Antonia M.} and {Vincent Poor}, H.",
note = "Funding Information: Ke Sun acknowledges the support of China Scholarship Council (CSC) and the support from the Department of Automatic Control and Systems Engineering for travelling. H.Vincent Poor was supported in part by the U.S. National Science Foundation under Grants DMS-1736417 and ECCS-1824710. Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682919",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8102--8106",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "United States",
}