DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

Abstract

Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified open framework integrating six processing layers: RF signal classification, acoustic motor-signature detection, YOLOv8-based visual detection, evidence-weighted sensor fusion, a Behavioral Intent Classification Engine (BICE), and a Graph Neural Network Swarm Intelligence Module (GNN-SIM). This v2 revision reports measured results on the completed implementation (495 automated tests), superseding the v1 simulation-only preprint. On real public data: RF presence detection reaches F1 0.9924; acoustic detection reaches 98.12% accuracy, though a 4-feature statistical baseline reaches 93.25% on the same split, so the model's drone-specific contribution is approximately 4.9 points, a property of dataset provenance rather than a leakage bug; visual detection (YOLOv8) reaches AP50 0.8782 on a held-out test split, from a model still improving when training stopped. No fused-accuracy figure is reportable, since the three real-data layers use independently collected, unsynchronized datasets with no shared ground truth. BICE and GNN-SIM remain evaluated on physics-calibrated simulation data pending real adversarial and swarm field data: BICE shows a consistent robustness advantage over a trivial baseline under distribution shift; GNN-SIM underperforms its own trivial baseline in-distribution but independently replicates BICE's noise-robustness advantage, regarded as this work's most durable finding. All code, trained checkpoints, and the full test suite are publicly released.