Accurate identification of critical malicious drones is crucial for optimizing directed energy attacks and maximizing their effectiveness. However, current studies on critical drone identification are still in the preliminary stage and almost rely on the traditional centrality methods that do not address the distributed features of drone swarms. This leads to inaccurate identification of critical drones, resulting in the low efficiency of directed energy attacks. Therefore, this paper proposes a new critical drone identification method based on the distributed features, communication intensity, and communication scale of drones. Specifically, this paper first constructs a dynamic communication prediction network (DCPN) of drone swarms based on the 3D position and interaction range, which predicts the dynamic communication between drones. Then, this paper proposes a new method called dynamic giant connected component (GCC)-based scale-intensity centrality (DGSIC) that combines the local, global, and community structure of DCPN to identify critical nodes with stronger communication capabilities. The dynamic strategy involves the iterative identification of one critical node at each step, considering the evolving network configuration and ensuring the identified node remains the most critical in the present network. Additionally, the prioritization strategy is employed to identify the nodes within the GCC, which can significantly impact the network connectivity and communication. DGSIC optimizes the attack sequence for directed energy attacks, facilitating the rapid dissolution of malicious drone swarms. Extensive experiments in four simulated networks and eight real-world networks demonstrate the superior robustness and cascading failure performance of DGSIC.
