Adversarial Attacks Targeting Point-to-Point Wireless Networks

Ahmad Ghasemi, Majid Moradikia, Seyed Reza Zekavat, Hossein Pishro-Nik

Abstract: This paper introduces a novel adversarial attack targeting Graph Neural Network (GNN)-based radio resource management in point-to-point networks. Our proposed attack, executed during the test phase, manipulates the system's input by exploiting specific constraints. Formulated as an optimization problem, the attack aims to maximize resource stealing, thereby degrading the quality of communication. We assess the attack's efficacy with respect to the number of users, signal-to-noise ratio, and the adversary's power budget. The results demonstrate that our proposed attack approaches the performance of an established upper-bound adversarial benchmark while maintaining lower complexity, highlighting its effectiveness and potential for real-world applicability.