Divide-and-Conquer Secure Distributed Estimation With Density Peak Under Adversarial Networks

Secure distributed estimation algorithms have garnered widespread attention for their stability and security. However, in the harsh adversarial network environment, the network is subjected to multiple attacks, and a large number of nodes are compromised, leading to a significant reduction in the estimation performance of traditional secure algorithms. To tackle this problem, a divide-and-conquer secure distributed estimation with density peak (DCSDP) algorithm is proposed. Specifically, a divide-and-conquer attack defense (DCAD) strategy is proposed, in which two specialized attack detection steps are designed to divide and process the compromised data in the network. Considering the presence of multiple attacks in the network, a density peak-based attack detection scheme is proposed and embedded into the DCAD strategy. Additionally, by constructing adaptive thresholds and introducing a trust mechanism, the robustness of the algorithm is enhanced. Finally, the performance of the proposed algorithm is theoretically analyzed in terms of the mean and mean-square. Simulation experiments demonstrated that DCSDP maintains excellent robustness and effectiveness in different adversarial network environments.