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.

中文翻译：

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下行MU-MISO系统的学习辅助监听和符号级预编码对策

在这项工作中，我们介绍了一种基于机器学习（ML）的检测攻击，其中窃听者（Eve）能够基于预编码的飞行员学习符号检测功能。借助此功能，夏娃可以高概率正确地检测符号。为了应对这种攻击，我们提出了一种新颖的符号级预编码（SLP）方案，该方案可增强物理层安全性（PLS），同时保证对目标用户的建设性干扰效果。与传统的SLP方案相反，所提出的方案对基于ML的攻击具有鲁棒性。特别地，提出的方案通过将夏娃的接收信号设计为位于检测区域的边界来增强安全性。这种独特的设计使Eve的检测决策几乎完全基于噪声。然后，将提出的对策扩展到考虑夏娃的多天线问题以及多级调制方案。在数值结果中，我们同时验证了检测攻击和对策，并表明，可以以仅在发射机处消耗少量额外功耗为代价来实现这种安全性增益，更重要的是，在不影响性能的情况下获得了这些好处。针对目标用户。