Title | Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Moradi, Ashkan, Venkategowda, Naveen K. D., Werner, Stefan |
Conference Name | 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Date Published | dec |
Keywords | adaptive filtering, attack sequences, attack strategy, backward stepwise based subset selection method, block-coordinate descent method, Boolean algebra, Boolean relaxation, Byzantine agents, consensus-based distributed Kalman filtering, coordinated data-falsification attacks, Covariance matrices, data-falsification attack, Distributed databases, estimation error, false-data detectors, honest agents, Kalman filters, linear model, Metrics, naive attacks, neighboring agents, network-wide estimation error maximization, pubcrawl, Resiliency, Scalability, Sensors, state estimation, telecommunication security, uncoordinated attacks |
Abstract | 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. |
DOI | 10.1109/CAMSAP45676.2019.9022448 |
Citation Key | moradi_coordinated_2019 |