Stealth Data Injection Attacks with Sparsity Constraints - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Smart Grid Year : 2023

Stealth Data Injection Attacks with Sparsity Constraints

Abstract

Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlation between the attack vector entries and achieves a better disruption to stealth tradeoff at the cost of requiring communication between different locations. We numerically evaluate the performance of the proposed attack constructions on IEEE test systems and show that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors.
Fichier principal
Vignette du fichier
YEPH_TSG_21_submission.pdf (297.12 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03516567 , version 1 (07-01-2022)

Identifiers

Cite

Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza, Robert F Harrison. Stealth Data Injection Attacks with Sparsity Constraints. IEEE Transactions on Smart Grid, 2023, 14 (4), pp.3201 -- 3209. ⟨10.1109/TSG.2023.3238913⟩. ⟨hal-03516567⟩
119 View
99 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More