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Conference Papers Year : 2020

Information Theoretic Data Injection Attacks with Sparsity Constraints


Information theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case.
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Dates and versions

hal-03128257 , version 1 (02-02-2021)


  • HAL Id : hal-03128257 , version 1


Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza, Robert F Harrison. Information Theoretic Data Injection Attacks with Sparsity Constraints. SmartGridComm 2020 - International Conference on Communications, Control, and Computing Technologies for Smart Grids, Nov 2020, Virtual Conference, United States. ⟨hal-03128257⟩
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