Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering

This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzantine agents share manipulated data with their neighboring agents. The attack is assumed to be coordinated among the Byzantine agents and follows a linear model. The goal of the Byzantine agents is to maximize the network-wide estimation error while evading false-data detectors at honest agents. To that end, we propose a joint selection of Byzantine agents and covariance matrices of attack sequences to maximize the network-wide estimation error subject to constraints on stealthiness and the number of Byzantine agents. The attack strategy is then obtained by employing block-coordinate descent method via Boolean relaxation and backward stepwise based subset selection method. Numerical results show the efficiency of the proposed attack strategy in comparison with other naive and uncoordinated attacks.