Resilient Distributed Kalman Filtering Against Malicious Cyber Attacks

In this work, we consider the resilient distributed Kalman filtering (RDKF) for adversarial networks in the presence of different malicious cyber attacks, and develop an RDKF algorithm to enhance the network estimation accuracy. Specifically, we develop an attack detection approach such that each node would distinguish its secure neighbor(s) from its compromised counterpart(s), and determine whether it is compromised or not. We further propose a resilient fusion strategy to restrain the propagation of malicious intermediate estimates of each compromised node. We also theoretically analyze the mean and mean-square stability of the proposed RDKF algorithm, and develop an optimal reliable reference neighbor selection scheme, such that the network estimation accuracy could be further enhanced. Illustrative simulations validate the efficacy of the proposed algorithm for adversarial networks under different cyber attacks, even under potentially time-varying attacks.