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

• URL: http://arxiv.org/abs/2606.11687v1
• Date: Wed, 10 Jun 2026 06:00:24 GMT
• Title: 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 summary: DroneShield-AI is a unified open framework integrating six processing layers.<n>BICE introduces the first systematic six-class threat taxonomy for drone flight patterns.<n>GNN-SIM is the first open framework for adversarial multi-drone formation analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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). 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.

Related papers

• EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles [32.95664371081981]
  We introduce an Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) that decodes passenger neural responses for both Risk Prediction (RP) and Danger Identification (DI)<n>We propose the Passenger Cognitive Model (PCM), Risk-aware Sequential Labeling (RSL), and the Passenger EEG Decoding Strategy (PEDS)<n> Experimental results show that 3D-CRNN achieves a Balanced Accuracy (BA) of $95.3% pm 2.7%$ in RP and improves single-subject DI from $80.9% pm 3.9%$ to $85.0%
  arXiv  Detail & Related papers  (2026-09-07T07:22:04Z)
• City Sentinel: A Unified AI-Based Smart Surveillance Framework for Real-Time Multi-Threat Detection Using Deep Learning [0.06999740786886534]
  City Sentinel is a unified AI-based surveillance framework that integrates six detection capabilities into one scalable platform.<n>System achieves median end-to-end latency of 743 ms and supports four concurrent RTSP streams.<n>It achieves a 91.2% face-match rate, 85.7% plate-reading accuracy, and mAP@0.5 scores of 0.846 to 0.889 across the fire, knife, and weapon detection modules.
  arXiv  Detail & Related papers  (2026-08-09T19:59:31Z)
• A 6G Integrated Sensing and Communication Framework for Railway Intrusion Detection and Collision Prediction [2.1792156110421668]
  Integrated Sensing and Communication (ISAC) combines sensing and communication to efficiently utilize wireless resources.<n>ISAC enables physical-layer sensing using Channel State Information (CSI)<n>We develop a machine learning model combining a three-dimensional Convolutional Neural Network (3D CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network to detect intruders in the track danger zone.
  arXiv  Detail & Related papers  (2026-08-05T11:24:03Z)
• CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks [8.234024185555208]
  Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things (IIoT) networks.<n>Existing detection and classification techniques are poorly suited to this setting.<n>This paper presents a lightweight two-stage hierarchical pipeline that uses only Channel State Information (CSI) measurements.
  arXiv  Detail & Related papers  (2026-06-22T07:16:50Z)
• Secure UAV Swarms in Low-Altitude Wireless Networks: Challenges and Solutions [28.36153282686918]
  This paper proposes a cloud-edge-end collaborative defense framework for UAV swarms.<n>First, a cooperative perception scheme is designed to resist GPS spoofing.<n>Second, a behavior-driven authentication method with trust evaluation is developed to mitigate insider threats.<n>Third, a multi-agent attack forensics framework is devised to intelligently trace the propagation paths of multi-hop attacks in UAV networks.
  arXiv  Detail & Related papers  (2026-05-26T11:33:02Z)
• Characterizing AI-Assisted Bot Traffic in Darknet Data: Implications for ICS and IIoT Security [0.0]
  The rise of automated scanning tools and AI assisted reconnaissance agents has significantly altered internet background traffic patterns.<n>This paper characterizes the evolution of automated bot traffic by analyzing a longitudinal dataset of 192 million passive darknet packets captured across 2021 and 2025 from the Merit ORION Network Telescope.
  arXiv  Detail & Related papers  (2026-05-14T00:07:20Z)
• Instance-level Visual Active Tracking with Occlusion-Aware Planning [61.982298426203165]
  Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space.<n>VAT faces two key bottlenecks in real-world deployment: confusion from visually similar distractors and severe failure under occlusions.<n>We propose OA-VAT, a unified pipeline with three complementary modules.
  arXiv  Detail & Related papers  (2026-04-23T09:11:50Z)
• LanG -- A Governance-Aware Agentic AI Platform for Unified Security Operations [2.1142550386295853]
  This paper presents the LLM-assisted network Governance (LanG), an open-source, governance-aware agentic AI platform for unified security operations.<n>The platform supports multi-tenant isolation, role-based access, and fully local deployment.<n>Finetuned anomaly and threat detectors achieve weighted F1 scores of 99.0% and 91.0%, respectively, in intrusion-detection benchmarks.
  arXiv  Detail & Related papers  (2026-04-07T05:22:25Z)
• AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection [0.0]
  AEGIS is an Adversarial Entropy-Guided Immune System powered by a Thermodynamic Variance-Guided Hyperbolic Liquid State Space Model.<n>A pure C++ eBPF Harvester with zero-copy IPC bypasses the Python GIL, enabling a linear-time O(N) Mamba-3 core to process 64,000-packet swarms at line-rate.
  arXiv  Detail & Related papers  (2026-04-02T15:16:56Z)
• MI$^2$DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks [47.386868423451595]
  MI$2$DAS is a multi-layer intrusion detection framework that integrates anomaly-based hierarchical traffic pooling and open-set recognition.<n>Experiments conducted on the Edge-IIoTset dataset demonstrate strong performance across all layers.<n>These results showcase MI$2$DAS as an effective, scalable and adaptive framework for enhancing IIoT security.
  arXiv  Detail & Related papers  (2026-02-27T09:37:05Z)
• Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks [0.5505634045241287]
  Existing security schemes are unable to provide sub-10 ms anomaly detection and distributed coordination of large-scale networks of vehicles.<n>This paper introduces a three-tier hybrid security architecture HAVEN, which decouples real-time local threat detection and distributed coordination operations.<n>It incorporates a light ensemble anomaly detection model on the edge, Byzantine-fault-tolerant federated learning to aggregate threat intelligence at a regional scale, and selected blockchain mechanisms to ensure critical security coordination.
  arXiv  Detail & Related papers  (2025-11-16T15:30:46Z)
• A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks [0.24790748620223146]
  Existing intrusion detection mechanisms often lack the adaptability, efficiency, and generalizability required for drone networks.<n>This paper proposes TSLT-Net, a novel lightweight and unified Temporal Spatial Transformer based intrusion detection system tailored specifically for drone networks.
  arXiv  Detail & Related papers  (2025-10-03T04:23:21Z)
• End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection [41.94295877935867]
  End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$3$) We propose an End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$3$) It is a significant extension of A$3$ anomaly detection approach proposed by Sperl, Schulze and B"ottinger.
  arXiv  Detail & Related papers  (2024-11-21T10:22:50Z)
• MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats [37.981623262267036]
  MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation. It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB. Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools.
  arXiv  Detail & Related papers  (2024-02-06T04:57:07Z)
• CANShield: Signal-based Intrusion Detection for Controller Area Networks [29.03951113836835]
  We propose CANShield, a signal-based intrusion detection framework for the CAN bus. CanShield consists of three modules: a data preprocessing module that handles the high-dimensional CAN data stream at the signal level; a data analyzer module consisting of multiple deep autoencoder networks, each analyzing the time-series data from a different temporal perspective; and an attack detection module that uses an ensemble method to make the final decision.
  arXiv  Detail & Related papers  (2022-05-03T04:52:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.