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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

in 2019 IEEE Global Communications Conference (2019)

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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