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Machine Learning Based Attack Against Artificial Noise-aided Secure Communication

Wen, Yun, Yoshida, Makoto, Zhang, Junqing, Chu, Zheng, Xiao, Pei and Tafazolli, Rahim (2019) Machine Learning Based Attack Against Artificial Noise-aided Secure Communication In: 2019 IEEE International Conference on Communications (ICC): Communication and Information Systems Security Symposium (IEEE ICC 2019 - CISS Symposium), 20-24 May 2019, Shanghai, China.

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Abstract

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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Wen, Yun
Yoshida, Makoto
Zhang, Junqing
Chu, Zhengzheng.chu@surrey.ac.uk
Xiao, PeiP.Xiao@surrey.ac.uk
Tafazolli, RahimR.Tafazolli@surrey.ac.uk
Date : 20 May 2019
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Physical Layer Security; Artificial Noise; Machine Learning; Supervised-learning; Blind Estimation
Related URLs :
Depositing User : Clive Harris
Date Deposited : 06 Mar 2019 14:16
Last Modified : 20 May 2019 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/850679

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