Optimum Fusion of Possibly Corrupted Reports for Distributed Detection in Multi-Sensor Networks

TL;DR

This paper develops an optimal fusion rule for distributed detection in multi-sensor networks with malicious nodes, leveraging prior information about node behavior to improve decision accuracy despite corruption.

Contribution

It introduces a new optimal fusion method that exploits prior knowledge of malicious nodes, enhancing detection performance over traditional approaches.

Findings

Optimal fusion rule derived for independent and known number of malicious nodes

Performance analysis shows improved detection accuracy with prior information

Malicious nodes' strategies can be optimized to minimize impact on detection

Abstract

The most common approach to mitigate the impact that the presence of malicious nodes has on the accuracy of decision fusion schemes consists in observing the behavior of the nodes over a time interval T and then removing the reports of suspect nodes from the fusion process. By assuming that some a-priori information about the presence of malicious nodes and their behavior is available, we show that the information stemming from the suspect nodes can be exploited to further improve the decision fusion accuracy. Specifically, we derive the optimum fusion rule and analyze the achievable performance for two specific cases. In the first case, the states of the nodes (corrupted or honest) are independent of each other and the fusion center knows only the probability that a node is malicious. In the second case, the exact number of corrupted nodes is fixed and known to the fusion center. We also investigate the optimum corruption strategy for the malicious nodes, showing that always reverting the local decision does not necessarily maximize the loss of performance at the fusion center.