一种基于社区划分的无人机集群恶意节点检测方案

Runhui Zhao, Sijing Wang, Hong Wen
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引用次数: 1

摘要

随着无人机集群的广泛应用,针对无人机节点的恶意攻击也越来越频繁。针对大规模无人机集群中的恶意节点检测问题,提出了一种基于社区划分的恶意节点检测方案。首先,Leiden社区发现算法将飞行集群划分为多个社区;然后,提出了一种利用无人机通信拓扑和控制拓扑对集群节点重要性进行综合评估的节点重要性评估算法。最后,本文提出了一种利用无人机社区节点的重要性、信任度和剩余功率来选择社区领导节点的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Scheme for Detecting Malicious Nodes in UAV Clusters Based on Community Division
With the wide use of UAV clusters, malicious attacks against UAV nodes are becoming more and more frequent. Aiming at the problem of malicious node detection in large-scale UAV clusters, this paper proposes a malicious node detection scheme based on community division. Firstly, the Leiden community discovery algorithm divides the flight cluster into multiple communities. Then, this paper presents a combined node importance evaluation algorithm using UAV Communication topology and control topology to evaluate the importance of cluster nodes. Finally, this paper proposes a method to select community leader nodes by using the importance, trust degree and residual power of UAV community nodes.
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