Distributed filtering under false data injection attacks

Abstract

We consider the problem of network security for distributed filtering under false data injection attacks over a wireless sensor network. To resist the hostile attacks from a malicious attacker who can inject false data into communication channels according to a certain probability, we design a protector for each sensor based on the online innovation information from its neighboring sensors to decide whether to use the received data at each time. To guarantee the Gaussianity of the innovations, we use a stochastic rule to transform the threshold detection. We also provide a sufficient condition for the stability of the estimator equipped with the proposed protector under hostile attacks. Moreover, we find a critical attack probability above which the steady-state estimation error covariance will exceed a pre-set value. Finally, we compare the estimation performances among several protection strategies, and explore the relationship between the system parameters and the protection effect.