Information Theoretic Data Injection Attacks with Sparsity Constraints
- Resource Type
- Conference
- Authors
- Ye, Xiuzhen; Esnaola, Inaki; Perlaza, Samir M.; Harrison, Robert F.
- Source
- 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2020 IEEE International Conference on. :1-6 Nov, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Covariance matrices
Optimization
State estimation
Smart grids
Gaussian distribution
Random variables
Mutual information
- Language
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.