Abstract
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.
Highlights
T HE FIFTH generation (5G) of cellular networks aims at satisfying the wireless broadband demands of 2020 [1]
physical-layer security (PLS) is envisioned to be used as an additional layer of protection on top of the existing security methods based on cryptography
We take the average of the above quantity over a large number of symbol slots, i.e., Edn,H[Ptot], to obtain the frame-level total transmit power, which is averaged over a large number of channel realizations
Summary
T HE FIFTH generation (5G) of cellular networks aims at satisfying the wireless broadband demands of 2020 [1]. By 2022, there will be 28.5 billion connected devices [2]. In such a congested environment, unintended receivers (e.g., an eavesdropper (Eve)) may detect some sensitive information. Physical-layer security (PLS) has attracted much interest recently as a complement to security in higher layers of the network [3]. The essence of PLS is to use the randomness of the propagation channel to provide security at the physical layer, i.e., by minimizing the information leakage to the Eve. Namely, PLS is envisioned to be used as an additional layer of protection on top of the existing security methods based on cryptography. Most literature on PLS utilize information theoretic metrics, such as secrecy rate [5], for performance analysis [6]–[11]. We find only few work in the literature [12]–[14] that tackles the problem from a signal processing point of view
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