Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks

Autor:	Krishnan, Sivaram, Park, Jihong, Sagar, Subhash, Sherman, Gregory, Campbell, Benjamin, Choi, Jinho
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.01143 Zobrazit plný text záznamu View this record from Arxiv