Secure Low-altitude Maritime Communications via Intelligent Jamming

• URL: http://arxiv.org/abs/2511.06659v1
• Date: Mon, 10 Nov 2025 03:16:19 GMT
• Title: Secure Low-altitude Maritime Communications via Intelligent Jamming
• Authors: Jiawei Huang, Aimin Wang, Geng Sun, Jiahui Li, Jiacheng Wang, Weijie Yuan, Dusit Niyato, Xianbin Wang,
• Abstract summary: Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications.<n>The open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks.<n>We propose a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers.
• Score: 53.42658269206017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fails to capture the dynamic movement patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positioning to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications.

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