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Stealth Attacks on the Smart Grid

Authors :
H. Vincent Poor
Samir M. Perlaza
Ke Sun
Inaki Esnaola
Department of Automatic Control and Systems Engineering [ Sheffield] (ACSE)
University of Sheffield [Sheffield]
Department of Electrical and Computer Engineering [Princeton] (ECE)
Princeton University
Network Engineering and Operations (NEO )
Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Software and Cognitive radio for telecommunications (SOCRATE)
Inria Grenoble - Rhône-Alpes
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI)
Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)
Modèle et algorithmes pour des systèmes de communication fiables (MARACAS)
Source :
IEEE Transactions on Smart Grid, IEEE Transactions on Smart Grid, 2020, ⟨10.1109/TSG.2019.2935353⟩, IEEE Transactions on Smart Grid, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TSG.2019.2935353⟩
Publication Year :
2018
Publisher :
arXiv, 2018.

Abstract

Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables describing the grid. Simultaneously, the attacker aims to minimize the probability of attack detection by minimizing the Kullback-Leibler (KL) divergence between the distribution when the attack is present and the distribution under normal operation. The resulting cost function is the weighted sum of the mutual information and the KL divergence mentioned above. The tradeoff between the probability of attack detection and the reduction of mutual information is governed by the weighting parameter on the KL divergence term in the cost function. The probability of attack detection is evaluated as a function of the weighting parameter. A sufficient condition on the weighting parameter is given for achieving an arbitrarily small probability of attack detection. The attack performance is numerically assessed on the IEEE 30-Bus and 118-Bus test systems.<br />Comment: IEEE Trans. Smart Grid

Details

ISSN :
19493053 and 19493061
Database :
OpenAIRE
Journal :
IEEE Transactions on Smart Grid, IEEE Transactions on Smart Grid, 2020, ⟨10.1109/TSG.2019.2935353⟩, IEEE Transactions on Smart Grid, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TSG.2019.2935353⟩
Accession number :
edsair.doi.dedup.....be03133a41fea1d828d5f006334e6cad
Full Text :
https://doi.org/10.48550/arxiv.1808.04184