Deep Learning-Based Proactive Eavesdropping for Wireless Surveillance

Jihwan Moon, Sang Hyun Lee, Hoon Lee, Seunghwan Baek, Inkyu Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


In this work, we investigate a proactive eavesdropping system where a central monitor covertly wiretaps the communications between a pair of suspicious users via multiple intermediate nodes. For successful eavesdropping, it is required that the eavesdropping channel capacity is higher than the data rate of the suspicious users so that the central monitor can reliably decode the intercepted information. Hence, the intermediate nodes operate in two different modes, namely eavesdropping mode and jamming mode, to facilitate eavesdropping. Specifically, the eavesdropping nodes forward the intercepted data from the suspicious users to the central monitor, while the jamming nodes transmit jamming signals to proactively control the data rate of the suspicious users. We propose an efficient deep learning-based approach to identify the optimal mode selection for the intermediate nodes and the optimal transmit power for the jamming nodes. Numerical results confirm the significant performance gain of our proposed method both in terms of performance and time complexity over conventional schemes.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
Publication statusPublished - 2019 May
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: 2019 May 202019 May 24

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2019 IEEE International Conference on Communications, ICC 2019

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation through the Ministry of Science, ICT, and Future Planning (MSIP), Korean Government under Grant 2017R1A2B3012316.

Publisher Copyright:
© 2019 IEEE.


  • Deep learning
  • cooperative jamming
  • deep neural network
  • physical layer security
  • proactive eavesdropping
  • wireless surveillance

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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