Resilient Distributed Estimation Against FDI Attacks: A Game-Theoretic Approach

False data injection (FDI) attack is one of the major security threats to distributed estimation for adaptive networks, and could bypass the conventional residual-based detection and thus cause catastrophic performance deterioration. In this work, we develop a resilient distributed game-theoretic estimation algorithm comprising the Bayes-based FDI attack detection, the noncooperative game-based decision-making, and the resilient fusion strategy. Specifically, upon the detection of FDI attacks, we develop a distortionless instantaneous reward and distortionless long-term reward so as to facilitate the nodal decision-making on its action of whether to share its intermediate estimates. We further derive a best-response rule and benefit prediction scheme, such that each node could make its own independent decision on its current optimal action. Moreover, we develop a resilient fusion strategy to restrain the propagation of malicious data of each compromised node. Illustrative simulations validate the superior performance of the proposed algorithm for adversarial networks under FDI attacks.