[en] In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
MAYOUCHE, Abderrahmane ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
ALVES MARTINS, Wallace ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Tsinos, Christos G.
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
A Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures
Date de publication/diffusion :
2021
Nom de la manifestation :
IEEE Wireless Communications and Networking Conference (WCNC)
Date de la manifestation :
From 29-03-2021 to 01-04-2021
Sur invitation :
Oui
Manifestation à portée :
International
Titre de l'ouvrage principal :
IEEE Wireless Communications and Networking Conference (WCNC), Najing 29 March to 01 April 2021
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Projet FnR :
FNR11607830 - Exploiting Interference For Physical Layer Security In 5g Networks, 2017 (01/02/2018-31/07/2021) - Bjorn Ottersten
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