Detection-Guided Resilient Consensus Control for UAV Swarms Under Random Denial-of-Service Attacks
TL;DR
A detection-guided resilient consensus control framework for UAV swarms under R-DoS attacks and hardware-in-the-loop simulation experiments demonstrate that the proposed method can accurately detect attacked nodes, effectively reconstruct the communication topology, and maintain stable formation coordination under severe R-DoS attacks.
Abstract
Reliable coordination of unmanned aerial vehicle (UAV) swarms is significantly challenged by random denial-of-service (R-DoS) attacks, which introduce stochastic packet loss, time-varying communication interruptions, and strong concealment. Existing fault-tolerant consensus approaches typically assume known attack information or treat attack detection, topology recovery, and control design as separate processes, resulting in limited resilience under dynamically evolving attack conditions. To address this issue, this paper proposes a detection-guided resilient consensus control framework for UAV swarms under R-DoS attacks. A dual-dimensional statistical detection method is developed by jointly modeling packet reception rate (PRR) and inter-arrival time (IAT), enabling real-time identification of attack-induced anomalies through spatio-temporal feature fusion. Based on the detection results, a distributed topology reconstruction strategy is designed, incorporating redundant node identification and cluster-based dynamic communication reconfiguration. The communication graph is adaptively updated via online adjustment of adjacency and Laplacian matrices, and robustness guarantees for the resulting consensus process are analytically established. Hardware-in-the-loop simulation experiments under both single-leader and multi-leader architectures demonstrate that the proposed method can accurately detect attacked nodes, effectively reconstruct the communication topology, and maintain stable formation coordination under severe R-DoS attacks. The position tracking error is constrained within 0.4 m, validating the effectiveness and robustness of the proposed framework. This study is limited to defensive cyber-resilience in a closed hardware-in-the-loop simulation environment and does not address reconnaissance payloads, weaponization, target selection, or operational attack execution. Unlike methods that assume known attack schedules or treat detection, topology recovery, and control separately, this study focuses on their online coupling under unknown random packet loss; its validation is limited to the stated closed HIL impairment model.