Secure Low-altitude Maritime Communications via Intelligent Jamming

TL;DR

This paper proposes an intelligent jamming strategy using deep reinforcement learning to secure low-altitude maritime UAV communications against dynamic eavesdroppers, balancing security and energy efficiency.

Contribution

It introduces a novel SAC-CVAE deep reinforcement learning algorithm to optimize secure and energy-efficient maritime UAV communications under uncertain eavesdropper positions.

Findings

Achieves improved physical layer security in maritime LAWNs.

Balances secrecy rate and energy consumption effectively.

Demonstrates the effectiveness of SAC-CVAE in dynamic environments.

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.