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See detailLearning-Assisted Eavesdropping and Symbol-Level Precoding Countermeasures for Downlink MU-MISO Systems
Mayouche, Abderrahmane UL; Spano, Danilo UL; Tsinos, Christos UL et al

in IEEE Open Journal of the Communications Society (2020), 1

In this work, we introduce a machine-learning (ML) based detection attack, where an eavesdropper (Eve) is able to learn the symbol detection function based on precoded pilots. With this ability, an Eve ... [more ▼]

In this work, we introduce a machine-learning (ML) based detection attack, where an eavesdropper (Eve) is able to learn the symbol detection function based on precoded pilots. With this ability, an Eve can correctly detect symbols with a high probability. To counteract this attack, we propose a novel symbol-level precoding (SLP) scheme that enhances physical-layer security (PLS) while guaranteeing a constructive interference effect at the intended users. Contrary to conventional SLP schemes, the proposed scheme is robust to the ML-based attack. In particular, the proposed scheme enhances security by designing Eve's received signal to lie at the boundaries of the detection regions. This distinct design causes Eve's detection decisions to be based almost purely on noise. The proposed countermeasure is then extended to account for multi-antennas at the Eve and also for multi-level modulation schemes. In the numerical results, we validate both the detection attack and the countermeasures and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter, and more importantly, these benefits are obtained without affecting the performance at the intended user. [less ▲]

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Full Text
Peer Reviewed
See detailMachine learning for physical-layer security: Attacks and SLP Countermeasures for Multiantenna Downlink Systems
Mayouche, Abderrahmane UL; Spano, Danilo; Tsinos, Christos UL et al

Scientific Conference (2019, December)

Most physical-layer security (PLS) work employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on ... [more ▼]

Most physical-layer security (PLS) work employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on bit-error rate (BER) at the eavesdropper (Eve) as a metric for information leakage. Meanwhile, recently, symbol-level precoding (SLP) has been shown to provide PLS gains as a new way for security. However, in this work, we introduce a machine learning (ML) based attack, where we show that even SLP schemes can be vulnerable to such attacks. Namely, this attack manifests when an eavesdropper (Eve) utilizes ML in order to learn the precoding pattern when precoded pilots are sent. With this ability, an Eve can decode data with favorable accuracy. As a countermeasure to this attack, we propose a novel precoding design. The proposed countermeasure yields high BER at the Eve, which makes symbol detection practically infeasible for the latter, thus providing physical-layer security between the base station (BS) and the users. In the numerical results, we validate both the attack and the countermeasure, and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter. [less ▲]

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