Secure Low-altitude Maritime Communications via Intelligent Jamming

2025-11-10

AI Summary

Low-altitude wireless networks over the sea (LAWNs) face severe physical-layer security threats from dynamic eavesdroppers, yet existing approaches suffer from poor adaptability due to reliance on pre-specified eavesdropper trajectories. Method: This paper proposes an intelligent jamming framework that jointly optimizes UAV secrecy rate and energy efficiency. We formulate the problem as a partially observable Markov decision process (POMDP) to enhance online responsiveness to uncertain eavesdropping behavior. Crucially, we integrate generative AI into reinforcement learning by designing SAC-CVAE—a Soft Actor-Critic algorithm augmented with a Conditional Variational Autoencoder (CVAE)—to extract advantage-conditioned latent variables, enabling policy representation disentanglement and state-space dimensionality reduction. Contribution/Results: Experiments demonstrate significant improvements under dynamic eavesdropping: +23.6% in secrecy rate and +18.4% in energy efficiency ratio, establishing a highly robust security solution for maritime low-altitude communications.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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.

Problem

• Securing vulnerable UAV communication channels against eavesdropping in maritime environments
• Balancing conflicting performance metrics between secrecy rate and UAV energy consumption
• Countering dynamic eavesdroppers with uncertain positioning using intelligent jamming techniques

Innovation

• Intelligent jamming counters dynamic eavesdroppers positioning
• SAC-CVAE algorithm optimizes policies via latent representations
• Generative AI enhances deep reinforcement learning efficiency