  The successful emergence of deep learning (DL) in wireless system
applications has raised concerns about new security-related challenges. One
such security challenge is adversarial attacks. Although there has been much
work demonstrating the susceptibility of DL-based classification tasks to
adversarial attacks, regression-based problems in the context of a wireless
system have not been studied so far from an attack perspective. The aim of this
paper is twofold: (i) we consider a regression problem in a wireless setting
and show that adversarial attacks can break the DL-based approach and (ii) we
analyze the effectiveness of adversarial training as a defensive technique in
adversarial settings and show that the robustness of DL-based wireless system
against attacks improves significantly. Specifically, the wireless application
considered in this paper is the DL-based power allocation in the downlink of a
multicell massive multi-input-multi-output system, where the goal of the attack
is to yield an infeasible solution by the DL model. We extend the
gradient-based adversarial attacks: fast gradient sign method (FGSM), momentum
iterative FGSM, and projected gradient descent method to analyze the
susceptibility of the considered wireless application with and without
adversarial training. We analyze the deep neural network (DNN) models
performance against these attacks, where the adversarial perturbations are
crafted using both the white-box and black-box attacks.
