Generative Diffusion Models for Secure UAV-ISAC with Real-Time Target Tracking
Abstract
Unmanned aerial vehicle (UAV) platforms with integrated sensing and communication (ISAC) are prime candidates for surveillance missions, in which a dual-function drone tracks a mobile target and alerts a ground station when the target enters a protected region. Such alerts generate event-triggered communication traffic. By observing the change in the traffic, an eavesdropper (Eve) can therefore learn in real time whether the target is detected inside the protected region without decoding any message. Existing physical-layer security techniques protect the message content or hide that a transmission occurs, but the composition of the traffic remains exposed. We formulate the threat as traffic-composition covertness and propose a keyless diffusion-based transmission scheme that hides the triggered traffic. The scheme superposes the beamformed information streams of the UAV with a message-agnostic safeguard waveform generated by a conditional denoising diffusion implicit model. Training shapes the safeguard distribution such that triggered and non-triggered slots become statistically indistinguishable at Eve. The legitimate user shares no secret key with the UAV. We formulate the joint design as a rate maximization under covertness, reliability, and sensing constraints, with the sensing accuracy measured by the posterior Cramér-Rao bound (PCRB) of the beam-dependent tracking problem. Our numerical results show that, with multiple transmit antennas and an Eve signal-to-noise ratio (SNR) of $5$~dB, a learned Eve stays near the blind guess of $0.5$ for every safeguard beam split, a detector that knows the safeguard model reaches $0.71$ to $0.73$ inside Bob's main lobe and $0.53$ to $0.55$ outside it, while the average position PCRB varies only between $0.59$ and $0.70m^2$.
Related
- Sensing-Aided Secure Multicast in Rotatable Antenna-Enabled ISAC Systems
- Federated Calibration of Motion Uncertainty for UAV Tracking in Multi-Operator ISAC Systems
- UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems
- DiffLyap-MEC: Diffusion-Guided Lyapunov Optimization for Joint Offloading, Semantic Compression, and Resource Allocation in ISAC-Enabled 6G Edge Computing
- ECPD-MIE: An adaptive entropy-controlled chaotic permutation-diffusion scheme for secure multi-image encryption of grayscale and color images
BibTeX
@misc{nikbakht2026generative,
title = {{Generative Diffusion Models for Secure UAV-ISAC with Real-Time Target Tracking}},
author = {Homa Nikbakht and Yonina C. Eldar and H. Vincent Poor},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.28309},
eprint = {2610.28309},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.28309},
}
RIS
TY - UNPB
TI - Generative Diffusion Models for Secure UAV-ISAC with Real-Time Target Tracking
AU - Homa Nikbakht
AU - Yonina C. Eldar
AU - H. Vincent Poor
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.28309
UR - https://arxiv.org/abs/2610.28309
AB - Unmanned aerial vehicle (UAV) platforms with integrated sensing and communication (ISAC) are prime candidates for surveillance missions, in which a dual-function drone tracks a mobile target and alerts a ground station when the target enters a protected region. Such alerts generate event-triggered communication traffic. By observing the change in the traffic, an eavesdropper (Eve) can therefore learn in real time whether the target is detected inside the protected region without decoding any message. Existing physical-layer security techniques protect the message content or hide that a transmission occurs, but the composition of the traffic remains exposed. We formulate the threat as traffic-composition covertness and propose a keyless diffusion-based transmission scheme that hides the triggered traffic. The scheme superposes the beamformed information streams of the UAV with a message-agnostic safeguard waveform generated by a conditional denoising diffusion implicit model. Training shapes the safeguard distribution such that triggered and non-triggered slots become statistically indistinguishable at Eve. The legitimate user shares no secret key with the UAV. We formulate the joint design as a rate maximization under covertness, reliability, and sensing constraints, with the sensing accuracy measured by the posterior Cramér-Rao bound (PCRB) of the beam-dependent tracking problem. Our numerical results show that, with multiple transmit antennas and an Eve signal-to-noise ratio (SNR) of $5$~dB, a learned Eve stays near the blind guess of $0.5$ for every safeguard beam split, a detector that knows the safeguard model reaches $0.71$ to $0.73$ inside Bob's main lobe and $0.53$ to $0.55$ outside it, while the average position PCRB varies only between $0.59$ and $0.70m^2$.
ER -