Secure distributed estimation against false data injection attack

Abstract With the development of wireless sensor networks, many distributed algorithms have been studied by researchers. This paper considers the situation of distributed estimation with false data injection (FDI) attack. Owing to the fact that Kullback-Leibler (KL) divergence is very effective to detect outliers caused by FDI attack, a distributed adaptive algorithm over KL divergence is proposed to detect FDI attack. When the malicious nodes are detected, three algorithms are explored separately to weaken the impact of FDI attack. The performance of the three algorithms is analyzed in mean and mean-square. The effectiveness of the three proposed algorithms is shown through some illustrative examples under continual FDI attack and time-sharing FDI attack.

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