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Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

机译：基于联合的学习的学习认知检测飞行ad-hoc网络中的干扰攻击

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摘要

Flying Ad-hoc Network (FANET) is a decentralized communication system solely formed by Unmanned Aerial Vehicles (UAVs). In FANET, the UAV clients are vulnerable to various malicious attacks such as the jamming attack. The aerial adversaries in the jamming attack disrupt the communication of the victim network through interference on the receiver side. Jamming attack detection in FANET poses new challenges for its key differences from other ad-hoc networks. First, because of the varying communication range and power consumption constraints, any centralized detection system becomes trivial in FANET. Second, the existing decentralized solutions, disregarding the unbalanced sensory data from new spatial environments, are unsuitable for the highly mobile and spatially heterogeneous UAVs in FANET. Third, given a huge number of UAV clients, the global model may need to choose a sub-group of UAV clients for providing a timely global update. Recently, federated learning has gained attention, as it addresses unbalanced data properties besides providing communication efficiency, thus making it a suitable choice for FANET. Therefore, we propose a federated learning-based on-device jamming attack detection security architecture for FANET. We enhance the proposed federated learning model with a client group prioritization technique leveraging the Dempster-Shafer theory. The proposed client group prioritization mechanism allows the aggregator node to identify better client groups for calculating the global update. We evaluated our mechanism with datasets from publicly available standardized jamming attack scenarios by CRAWDAD and the ns-3 simulated FANET architecture and showed that, in terms of accuracy, our proposed solution (82:01% for the CRAWDAD dataset and 89.73% for the ns-3 simulated FANET dataset) outperforms the traditional distributed solution (49.11% for the CRAWDAD dataset and 65.62% for the ns-3 simulated FANET dataset). Moreover, the Dempster-Shafer-based client group prioritization mechanism identifies the best client groups out of 56 client group combinations for efficient federated averaging.
机译：飞行ad-hoc网络（FANET）是由无人驾驶飞行器（无人机）形成的分散式通信系统。在FANET中，UAV客户端容易受到各种恶意攻击，如干扰攻击。干扰攻击中的空中对手扰乱了受害者网络的通信，通过干扰对接收器侧。扇形中的干扰攻击检测对其与其他Ad-hoc网络的关键差异构成了新的挑战。首先，由于沟通范围和功耗约束不同，因此任何集中检测系统在烟雾中变得微不足道。其次，现有的分散解决方案忽视来自新空间环境的不平衡的感官数据，不适合粉丝中高度移动和空间异构的无人机。第三，给定大量的UAV客户端，全局模型可能需要选择一个UAV客户端的子组，以便提供及时的全局更新。 Recently, federated learning has gained attention, as it addresses unbalanced data properties besides providing communication efficiency, thus making it a suitable choice for FANET.因此，我们提出了一种基于联合的学习基于设备的攻击攻击检测安全架构。我们通过利用Deppster-Shafer理论的客户组优先级技术提升所提出的联合学习模型。所提出的客户组优先级化机制允许聚合器节点识别用于计算全局更新的更好的客户组组。我们评估了通过Crawdad和NS-3模拟扇形架构的公开可用的标准化干扰攻击场景的数据集的机制，并在准确性方面，我们提出的解决方案（爬行数据集82：01％，对于NS而89.73％ -3模拟扇形数据集）优于传统的分布式解决方案（爬行数据集的49.11％，NS-3模拟粉丝数据集的65.62％）。此外，基于Dempster-Shafer的客户组优先级机制确定了56个客户组合中的最佳客户组，以实现高效联合平均。

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