Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks. Unlike existing wireless security techniques, LPD measures aim to conceal the entire existence of wireless communication instead of safeguarding the information transmitted from users. Motivated by LPD communication, in this paper, 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 and predict an optimal communication region for each node in the wireless network, allowing them to communicate while remaining undetected from external actors. We also demonstrate the effectiveness of the proposed method in terms of two performance measures, i.e., mean absolute error and median absolute error.

本文研究了一种基于图神经网络的、保护隐私并分布式的框架，用于最小化无线自组织网络的可探测性，并为网络中的每个节点预测最佳的通信区域，使它们在与外部通信时保持不被探测的状态。研究结果表明，所提出的方法在平均绝对误差和中位绝对误差这两个方面具有很好的效果。

面向无线自组网低探测概率的联邦图学习