Covert Communication for Untrusted UAV-Assisted Wireless Systems
- URL: http://arxiv.org/abs/2403.09475v1
- Date: Thu, 14 Mar 2024 15:17:56 GMT
- Title: Covert Communication for Untrusted UAV-Assisted Wireless Systems
- Authors: Chan Gao, Linying Tian, Dong Zheng,
- Abstract summary: UAV-assisted covert communication is a supporting technology for improving covert performances.
This paper investigates the performance of joint covert and security communication in a tow-hop UAV-assisted wireless system.
- Score: 1.2190851745229392
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Wireless systems are of paramount importance for providing ubiquitous data transmission for smart cities. However, due to the broadcasting and openness of wireless channels, such systems face potential security challenges. UAV-assisted covert communication is a supporting technology for improving covert performances and has become a hot issue in the research of wireless communication security. This paper investigates the performance of joint covert and security communication in a tow-hop UAV-assisted wireless system, where a source transmits the covert message to a destination with the help of an untrusted UAV. We first design a transmission scheme such that use UAVs to assist in covert communications while ensuring the security of covert messages. Then, we develop a theoretical model to derive the expressions for the detection error probability of the warden and the covert and security rate, and the maximum covert and security rate is optimized by power control under a given covertness and security requirements. Finally, numerical results are provided to illustrate our theoretical analysis and the performance of covert and security communication in such systems.
Related papers
- Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks [82.3753728955968]
We introduce a novel Mixture-of-Experts (MoE)-based SemCom system.
This system comprises a gating network and multiple experts, each specializing in different security challenges.
The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements.
A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system.
arXiv Detail & Related papers (2024-09-24T03:17:51Z) - Physical Layer Deception with Non-Orthogonal Multiplexing [52.11755709248891]
We propose a novel framework of physical layer deception (PLD) to actively counteract wiretapping attempts.
PLD combines PLS with deception technologies to actively counteract wiretapping attempts.
We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches.
arXiv Detail & Related papers (2024-06-30T16:17:39Z) - Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts [80.0638227807621]
generative artificial intelligence (GAI) models have demonstrated superiority over conventional AI methods.
MoE, which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions.
arXiv Detail & Related papers (2024-05-07T11:13:17Z) - Towards Secure and Reliable Heterogeneous Real-time Telemetry Communication in Autonomous UAV Swarms [0.0]
This paper evaluates UAV peer-to-peer telemetry communication, highlighting its security vulnerabilities.
We suggest a symmetric key agreement and data encryption mechanism implementation for inter - swarm communication.
arXiv Detail & Related papers (2024-04-11T08:37:22Z) - Graph Koopman Autoencoder for Predictive Covert Communication Against
UAV Surveillance [29.15836826461713]
Low Probability of Detection (LPD) communication aims to obscure the very presence of radio frequency (RF) signals.
Unmanned Aerial Vehicles (UAVs) can detect RF signals from the ground by hovering over specific areas of interest.
We introduce a novel framework that combines graph neural networks (GNN) with Koopman theory to predict the trajectories of multiple fixed-wing UAVs.
arXiv Detail & Related papers (2024-01-23T23:42:55Z) - Will 6G be Semantic Communications? Opportunities and Challenges from
Task Oriented and Secure Communications to Integrated Sensing [49.83882366499547]
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) networks through the integration of multi-task learning.
We employ deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver.
We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases.
arXiv Detail & Related papers (2024-01-03T04:01:20Z) - Blockchain-Based Security Architecture for Unmanned Aerial Vehicles in B5G/6G Services and Beyond: A Comprehensive Approach [4.552065156611815]
Unmanned Aerial Vehicles (UAVs) have evolved into indispensable tools for effectively managing disasters and responding to emergencies.
It is substantial to identify and consider the different security challenges in the research and development associated with advanced UAV-based B5G/6G architectures.
arXiv Detail & Related papers (2023-12-12T01:55:04Z) - Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts [89.04751776308656]
This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding.
In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
arXiv Detail & Related papers (2023-09-05T23:24:56Z) - Federated Graph Learning for Low Probability of Detection in Wireless
Ad-Hoc Networks [36.82926581689718]
Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks.
We study a privacy-preserving and distributed framework based on graph neural networks to minimise the detectability of a wireless ad-hoc network as a whole.
arXiv Detail & Related papers (2023-06-01T20:56:02Z) - Aerial Base Station Positioning and Power Control for Securing
Communications: A Deep Q-Network Approach [3.234560001579256]
UAV will play a critical role in enhancing the physical layer security of wireless networks.
This paper defines the problem of eavesdropping on the link between the ground user and the UAV.
reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power.
arXiv Detail & Related papers (2021-12-21T10:53:58Z) - A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From
Communications to Sensing and Intelligence [152.89360859658296]
5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC)
On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in 3D space.
On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
arXiv Detail & Related papers (2020-10-19T08:56:04Z)
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.