FNIA: ferry node identification attack based on UAV flight features

The ferry UAV (Unmanned Aerial Vehicle) serves a pivotal function within UAV swarms, acting as a bridge between the ground station and regular UAVs. By facilitating communication, providing relay support, and transmitting data, the ferry node becomes a critical target for potential attacks. This paper proposes a novel method for ferry node identification attack based on flight characteristics. Given the complexity of the large dataset generated by the swarm, this approach begins with data pruning to reduce noise and enhance relevant features. Subsequently, flight characteristics are extracted through an analysis of time-domain data to capture key flight behaviors. A similarity matrix of flight features is then constructed using the Procrustes-based trajectory similarity method. Based on this matrix, existing clustering and classification algorithms are applied to accurately identify ferry nodes. Experimental results demonstrate high accuracy in detecting target ferry nodes, underscoring the method’s effectiveness in addressing this challenge.