Graph Attention Networks For Anomalous Drone Detection: RSSI-Based Approach with Real-world Validation

The swift proliferation of unmanned aerial vehicles (UAVs) and their expanding applications have engendered considerable security apprehensions, especially with the detection of anomalous drones inside swarms. This research introduces an innovative methodology utilising Graph Attention Networks (GAT) and Received Signal Strength Indicator (RSSI) data to discover and identify abnormal drones in UAV networks. The suggested method employs a V-cycle algorithm-based graph attention model, wherein RSSI deviations from the mean are calculated for each drone node and utilised as a feature within the graph. A radius graph is created to illustrate drone-to-drone conversations, facilitating the computation of attention scores that assess the significance of each node's connectivity and RSSI attributes. Drones displaying irregular RSSI patterns, as detected by the GAT framework, are identified as potential dangers or anomalous drones. The system is engineered to manage intricate real-world settings by effectively detecting drones exhibiting aberrant behaviour via multilevel graph coarsening and refinement methodologies. To assess the efficacy of the suggested strategy, simulations were executed, and empirical experiments were carried out with the Robolink Codrones kit. The trials validated the system's capability to identify drones exhibiting anomalous signal strength fluctuations in real-time situations. The findings illustrate the suggested method's efficacy in detecting anomalous drones using RSSI anomalies, surpassing conventional detection techniques in accuracy and computing efficiency. RSSI data and graph attention approaches for autonomous drone identification can improve UAV network security and anomaly detection systems, as shown in this study.