  In this work, we consider secure communications 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 soft and hard decoding schemes by using precoded
pilot symbols as training data. In this context, we propose ML frameworks for
decoders that allow an Eve to determine the transmitted message with high
accuracy. We thereby show that MU-MISO systems are vulnerable to such
eavesdropping attacks even when relatively secure transmission techniques are
employed, such as symbol-level precoding (SLP). To counteract this attack, we
propose two novel SLP-based schemes that increase the bit-error rate at Eve by
impeding the learning process. We design these two security-enhanced schemes to
meet different requirements regarding complexity, security, and power
consumption. Simulation results validate both the ML-based eavesdropping
attacks as well as the countermeasures, and show that the gain in security is
achieved without affecting the decoding performance at the intended users.
