Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict

• URL: http://arxiv.org/abs/2606.17119v1
• Date: Mon, 15 Jun 2026 11:04:07 GMT
• Title: Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict
• Abstract summary: This study examines how Graph Neural Networks (GNNs) can be used to aid cybersecurity and drone management.<n>Based on an emulation-based case study, cyberattacks models were created to provoke the responses of the drones.<n>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.
• Score: 0.3441021278275805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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