Abstract
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 language | English |
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Title of host publication | 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538680889 |
DOIs | |
Publication status | Published - 2019 May |
Event | 2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China Duration: 2019 May 20 → 2019 May 24 |
Publication series
Name | IEEE International Conference on Communications |
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Volume | 2019-May |
ISSN (Print) | 1550-3607 |
Conference
Conference | 2019 IEEE International Conference on Communications, ICC 2019 |
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Country/Territory | China |
City | Shanghai |
Period | 19/5/20 → 19/5/24 |
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.
Keywords
- 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