A deep learning based covert communication method in internet of things

Chaowei Duan ()
Additional contact information
Chaowei Duan: Guangzhou Haige Communication Group Incorporated Company

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 2, No 1, 12 pages

Abstract: Abstract Communication security has played a more and more important role in modern wireless communication systems, especially in Internet of Things (IoT) for its privacy data security requirement. However, the IoT faces severe security threats due to the broadcast nature of radio propagation in wireless communication network. Therefore, physical layer security has been a hot topic nowadays which can effectively protect the private data transmission from jamming and eavesdropping at the physical layer of a wireless network. Inspired by the widely discussed deep learning based wireless communications, this paper adopts the widely used autoencoder framework for covert communications in IoT, which aims at waveform hiding for physical layer security. This proposed method employs deep complex neural networks (DCNNs) between legitimate users in IoT to jointly perform modulation, synchronization and demodulation. The generated covert signal produced by the DCNNs presents Gaussian statistics on both time and frequency domains. Therefore, the communication security is strongly guaranteed due to the difficulty of unauthorized detection and decoding for eavesdroppers. Moreover, computer simulations under single-user and multi-user cases demonstrate the effectiveness of this proposed deep learning based covert communication method, and the symbol error rate performance shows the superiority of our proposed method.

Keywords: Autoencoder; Deep learning; Covert communication; Internet of things; Physical layer security (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-025-01274-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:88:y:2025:i:2:d:10.1007_s11235-025-01274-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-025-01274-2

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().