Machine Learning Based Attack Against Artificial Noise-Aided Secure Communication

Yun Wen, Makoto Yoshida, Junqing Zhang, Zheng Chu, Pei Xiao, Rahim Tafazolli

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper's channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper's channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper's channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.

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 - May 2019
Externally publishedYes
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: 20 May 201924 May 2019

Publication series

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


Conference2019 IEEE International Conference on Communications, ICC 2019


  • Artificial Noise
  • Blind Estimation
  • Machine Learning
  • Physical Layer Security
  • Supervised-learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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