Robust Distributed Estimation of Wireless Sensor Networks Under Adversarial Attacks

This paper focuses on the parameter estimation problem in wireless sensor networks (WSNs) under adversarial attacks, considering the complexities of sensing and communication in challenging environments. In order to mitigate the impact of these attacks on the network, we propose a novel AP-DLMS algorithm with adaptive threshold attack detection and malicious punishment mechanism. The adaptive threshold is constructed using the observation matrix and network topology to detect the location of malicious attacks, while the standard reference estimation is designed to obtain the estimated deviation of each node. To mitigate the impact of data tampering on network performance, we introduce the honesty factor and punishment factor to combine the weights of normal nodes and malicious nodes respectively. Additionally, we propose a new probabilistic random attack model. Simulations are conducted to investigate the influence of key parameters in the adaptive threshold on the performance of the proposed AP-DLMS algorithm, and the mean square performance of the algorithm is analyzed under various attack models. The results demonstrate that the proposed algorithm exhibits strong robustness in adversarial networks, and the proposed attack model effectively demonstrates the impact of attacks.