With the rapid development and extensive application of unmanned aerial vehicles (UAVs), the issue of UAV swarm network security has become prominent. To protect the security of UAV swarm networks, effective network security defense measures are crucial. One key aspect is the assessment and monitoring of the network's security situation. However, most existing research focuses on the security of individual UAVs or detecting specific attacks, which fails to provide proactive protection for the network. To address this issue, we propose a UAV swarm network security situation assessment method, which combines the Transformer network with the optimization of the Aggregated Residual Transformations for Deep Neural Networks (ResNeXt) and squeeze-and-excitation (SE) structure (named TransReSE). By using multiple scale-cross convolution kernels, TransReSE can efficiently extract data features and improve situation assessment accuracy through the Transformer network. Experimental results from four public datasets have shown that TransReSE outperforms other schemes in terms of accuracy, recall, and F1. By assessing the value of the swarm network situation and the threat level, we can make faster, more effective decisions and proactively allocate resources to defend against UAV swarm network attacks.

中文翻译：

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使用 TransReSE 评估无人机群网络的安全态势：基于 Transformer-ResNeXt-SE 的方法


随着无人机的快速发展和广泛应用，无人机群体网络安全问题日益突出。为了保障无人机集群网络的安全，有效的网络安全防御措施至关重要。其中一个关键方面是网络安全状况的评估和监控。然而，现有研究大多集中于单个无人机的安全或检测特定攻击，未能为网络提供主动保护。为了解决这个问题，我们提出了一种无人机群体网络安全态势评估方法，该方法将Transformer网络与深度神经网络聚合残差变换（ResNeXt）和挤压激励（SE）结构的优化相结合（命名为TransReSE） 。通过使用多个尺度交叉的卷积核，TransReSE可以通过Transformer网络高效地提取数据特征并提高态势评估的准确性。四个公共数据集的实验结果表明，TransReSE 在准确性、召回率和 F1 方面优于其他方案。通过评估集群网络态势和威胁级别的价值，我们可以做出更快、更有效的决策并主动分配资源来防御无人机集群网络攻击。