  This paper introduces adversarial attacks targeting a Graph Neural Network
(GNN) based radio resource management system in point to point (P2P)
communications. Our focus lies on perturbing the trained GNN model during the
test phase, specifically targeting its vertices and edges. To achieve this,
four distinct adversarial attacks are proposed, each accounting for different
constraints, and aiming to manipulate the behavior of the system. The proposed
adversarial attacks are formulated as optimization problems, aiming to minimize
the system's communication quality. The efficacy of these attacks is
investigated against the number of users, signal-to-noise ratio (SNR), and
adversary power budget. Furthermore, we address the detection of such attacks
from the perspective of the Central Processing Unit (CPU) of the system. To
this end, we formulate an optimization problem that involves analyzing the
distribution of channel eigenvalues before and after the attacks are applied.
This formulation results in a Min-Max optimization problem, allowing us to
detect the presence of attacks. Through extensive simulations, we observe that
in the absence of adversarial attacks, the eigenvalues conform to Johnson's SU
distribution. However, the attacks significantly alter the characteristics of
the eigenvalue distribution, and in the most effective attack, they even change
the type of the eigenvalue distribution.
