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). BICE introduces the first systematic six-class threat taxonomy for drone flight patterns, enabling predictive operator alerts with a 30-second advance-warning horizon. GNN-SIM is the first open framework for adversarial multi-drone formation analysis using Graph Attention Networks. Evaluated on three publicly available real-world datasets, the fused pipeline achieves 96.1% detection accuracy, 3.2% false alarm rate, AUC-ROC: 0.981, and 142ms end-to-end latency on commodity CPU-class hardware at approximately $500-$780 USD total system cost. All code, model weights, and simulation datasets are publicly released at submission.

翻译：无人机威胁已成为21世纪的一项关键安全挑战。本文提出DroneShield-AI，一个统一的开放框架，集成了六个处理层：射频信号分类、声学电机特征检测、基于YOLOv8的视觉检测、证据加权传感器融合、行为意图分类引擎（BICE）以及图神经网络群体智能模块（GNN-SIM）。BICE首次为无人机飞行模式建立了系统性的六类威胁分类体系，能够实现提前30秒预警的操作员预测性警报。GNN-SIM是首个利用图注意力网络进行对抗性多无人机编队分析的开放框架。在三个公开可用的真实世界数据集上评估，该融合流水线在约500-780美元总系统成本、采用商用CPU级硬件上，实现了96.1%的检测准确率、3.2%的误报率、AUC-ROC为0.981，以及142毫秒的端到端延迟。所有代码、模型权重及仿真数据集在提交时均已公开发布。