Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks (GNNs) can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles (UAVs). By providing a bridge between structural understanding of graphical neural networks, this work has provided an integrated procedure that allows intrusion detection systems to educate on underlying network structures, identify malicious activity, and facilitates drone response measures. Based on an emulation-based case study, cyberattacks models were created to provoke the responses of the drones, which proved that graph-based learning can assist with the situational awareness, swarm coordination, and adaptive maneuver. According to the performance valuation, this method has a detection rate of 94.2, average area under the receiver operating characteristic (ROC) of 0.955 and an average response time of 1.4 seconds. Comparative experiments reveal that proposed GraphSAGE network is more effective than the Graphical Convolutional Networks (GCNs) and Graphical Attention Networks (GATs) in the identical situation. Such findings prove that graphical neural networks can be used to avert intrusion and response of dynamic cyber-physical systems.

翻译：物理网络系统在检测和即时响应方面带来了新的威胁与挑战。本研究探讨了图神经网络（GNNs）如何用于辅助由网络入侵和无人机（UAVs）构成的物理网络系统中的网络安全与无人机管理。通过在图神经网络的结构性理解之间建立桥梁，本文提出了一种集成流程，使入侵检测系统能够学习底层网络结构、识别恶意活动，并促进无人机响应措施的制定。基于仿真案例研究，我们构建了网络攻击模型以激发无人机响应，证明了基于图的学习有助于提升态势感知、蜂群协调和自适应机动能力。性能评估显示，该方法的检测率为94.2%，接收者操作特征曲线（ROC）下的平均面积为0.955，平均响应时间为1.4秒。对比实验表明，在相同情景下，所提出的GraphSAGE网络比图卷积网络（GCNs）和图注意力网络（GATs）更有效。这些发现证明了图神经网络可用于动态网络物理系统中的入侵防御与响应。