Quantitative Analysis of UAV Intrusion Mitigation for Border Security in 5G with LEO Backhaul Impairments
- URL: http://arxiv.org/abs/2510.14066v1
- Date: Wed, 15 Oct 2025 20:13:48 GMT
- Title: Quantitative Analysis of UAV Intrusion Mitigation for Border Security in 5G with LEO Backhaul Impairments
- Authors: Rajendra Upadhyay, Al Nahian Bin Emran, Rajendra Paudyal, Lisa Donnan, Duminda Wijesekera,
- Abstract summary: Uncooperative unmanned aerial vehicles pose emerging threats to critical infrastructure and border protection.<n>This paper analyzes detect-to-mitigate latency of such intrusions in a hybrid terrestrial-nonterrestrial satellite 5G system.
- Score: 0.8427427828815586
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Uncooperative unmanned aerial vehicles (UAVs) pose emerging threats to critical infrastructure and border protection by operating as rogue user equipment (UE) within cellular networks, consuming resources, creating interference, and potentially violating restricted airspaces. This paper presents minimal features of the operating space, yet an end-to-end simulation framework to analyze detect-to-mitigate latency of such intrusions in a hybrid terrestrial-non-terrestrial (LEO satellite) 5G system. The system model includes terrestrial gNBs, satellite backhaul (with stochastic outages), and a detection logic (triggered by handover instability and signal quality variance). A lockdown mechanism is invoked upon detection, with optional local fallback to cap mitigation delays. Monte Carlo sweeps across UAV altitudes, speeds, and satellite outage rates yield several insights. First, satellite backhaul outages can cause arbitrarily long mitigation delays, yet, to meet fallback deadlines, they need to be effectively bounded. Second, while handover instability was hypothesized, our results show that extra handovers have a negligible effect within the range of parameters we considered. The main benefit of resilience from fallback comes from the delay in limiting mitigation. Third, patrol UEs experience negligible collateral impact, with handover rates close to terrestrial baselines. Stress scenarios further highlight that fallback is indispensable in preventing extreme control-plane and physical security vulnerabilities: Without fallback, prolonged outages in the satellite backhaul delay lockdown commands, allowing rogue UAVs to linger inside restricted corridors for several seconds longer. These results underscore the importance of complementing non-terrestrial links with local control to ensure robust and timely response against uncooperative UAV intrusions.
Related papers
- Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
arXiv Detail & Related papers (2026-02-27T04:30:35Z) - Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks [58.70163955407538]
Malicious eavesdroppers pose a serious threat to private information via satellite-terrestrial networks (STNs)<n>We propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing.<n>We exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer.
arXiv Detail & Related papers (2026-01-06T10:30:41Z) - ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
arXiv Detail & Related papers (2025-09-16T08:52:13Z) - Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network [48.485907216785904]
Low Earth orbit (LEO) satellite constellations offer promising solutions with global coverage and reduced latency.<n>Yet struggle with intermittent coverage and intermittent communication windows due to orbital dynamics.<n>Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-ground communication and reliable radio frequency (RF) links for HAP-to-ground transmission.
arXiv Detail & Related papers (2025-09-16T06:16:56Z) - NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
arXiv Detail & Related papers (2025-06-23T14:28:30Z) - Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.72954660774002]
Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas.<n>Extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks.<n>This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle.
arXiv Detail & Related papers (2025-01-26T10:13:51Z) - Uncovering GNSS Interference with Aerial Mapping UAV [0.0]
We propose a method that combines advanced flight dynamics with high-performance consumer receivers to detect interference over large areas.
The proposed system can detect interference sources and map their area of influence, gaining situational awareness of poor quality or denied environments.
arXiv Detail & Related papers (2024-05-13T10:21:03Z) - Deep Reinforcement Learning for Joint Cruise Control and Intelligent
Data Acquisition in UAVs-Assisted Sensor Networks [0.0]
Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets) are experiencing significant growth in civil applications worldwide.
One major challenge in these scenarios is that the movements of UAVs affect channel conditions and result in packet loss.
Our proposal is to minimize packet loss by jointly optimizing the velocity controls and data collection schedules of multiple UAVs.
arXiv Detail & Related papers (2023-12-15T17:04:03Z) - Secure and Efficient Federated Learning in LEO Constellations using
Decentralized Key Generation and On-Orbit Model Aggregation [1.4952056744888915]
This paper proposes FedSecure, a secure FL approach designed for LEO constellations.
FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites.
It also reduces convergence delay drastically from days to only a few hours, yet achieving high accuracy of up to 85.35%.
arXiv Detail & Related papers (2023-09-04T21:36:46Z) - Learning-Based UAV Trajectory Optimization with Collision Avoidance and
Connectivity Constraints [0.0]
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints.
We propose a decentralized deep reinforcement learning approach to solve the problem.
arXiv Detail & Related papers (2021-04-03T22:22:20Z) - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning
for Non-Terrestrial Networks [51.05735925326235]
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation.
To maximize the end-to-end data rate, the satellite association and HAP location should be optimized.
We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
arXiv Detail & Related papers (2020-05-26T05:39:27Z)
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.