Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning

2024/01/31 by Han-Bae Kong, Kong, Justin, Terrence J. Moore +3 · 2 citations
Computer Science · #Advanced Steganography and Watermarking Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Privacy-Preserving Technologies in Data #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2402.10087

openalex publication_date 2024/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This letter investigates covert routing communications in a heterogeneous network where a source transmits confidential data to a destination with the aid of relaying nodes where each transmitter judiciously chooses one modality among multiple communication modalities. We develop a novel reinforcement learning-based covert routing algorithm that finds a route from the source to the destination where each node identifies its next hop and modality only based on the local feedback information received from its neighboring nodes. We show based on numerical simulations that the proposed covert routing strategy has only negligible performance loss compared to the optimal centralized routing scheme.

Cited by

• Deep Reinforcement Learning for Multi-flow Routing in Heterogeneous Wireless Networks
• Deep Reinforcement Learning Based Routing for Heterogeneous Multi-Hop Wireless Networks

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