Multiscale feature-based robust secure diffusion estimation with noisy input over adversarial networks

This paper studies secure distributed estimation over wireless sensor networks in adversarial and noisy environments where a subset of sensors may be invaded by malicious attacks. We build a system model for the problem and prove that the bias-compensation method can resist noisy input but fails in attack defence. To alleviate the impact of attacks, a multiscale feature-based attack detection algorithm is proposed consisting of two main steps. Firstly, a binary discrimination mechanism is proposed to split neighbors of each node into two groups by their relative-state whose perception explores and exploits delicate element-wised features of attack vector. To reduce computational costs, the classifier is designed in a tree shape. Specifically, at each internal tree-node, a generalized correntropy based similarity metric function is developed and compared with an optimal threshold. Secondly, based on features of the classified groups, an absolute-state decider is presented to detect the trust neighbors and thus secure information sharing can be achieved. Simulation results reveal the effectiveness and robustness of our proposed method compared with some state-of-the-art algorithms under various attack forms.