A Bayesian Neural Network for Robust Automatic Modulation Classification: Mitigating Adversarial Amplification

Abstract

In recent years, the rapid advancement of wireless communication technologies, particularly in the development of sixth-generation networks, brought about challenges in spectrum efficiency, security, and reliability. Machine learning-based automatic modulation classification (AMC) plays a critical role in addressing these challenges by enabling efficient signal classification in dynamic environments. However, such systems remain vulnerable to adversarial attacks, which can induce machine learning-based systems into making mistakes and, by doing so, compromise applications that rely on them. Accordingly, in this study, we propose a robust AMC framework based on Bayesian neural networks (BNN) to mitigate the impact of adversarial attacks. Our approach uses a regularization term on the weight variance of the BNN to reduce the likelihood of extreme weight values, thereby enhancing model stability in adversarial settings. We also incorporate the Sinh-Arcsinh Gaussian distribution as a flexible prior to control skewness and tail behavior, thus improving the trade-off between robustness and accuracy. Experimental evaluations against common white-box adversarial attacks, such as fast gradient sign method (FGSM), projected gradient descent (PGD), and automatic PGD (Auto-PGD), demonstrate that our proposed model outperforms conventional AMC models, achieving greater resilience in low perturbation-to-noise ratio conditions. Taken together, these findings highlight the potential of Bayesian methods in developing more secure and reliable intelligent wireless communication systems.

Authors

Azzam Mourad, Department of CS, Khalifa University, Abu Dhabi, United Arab Emirates, Lebanon

Georges Kaddoum, Electrical Engineering Department, École de Technologie Supèrieure, University of Quebec, Montreal, QC, Canada

Mohamed Chiheb Ben Nasr, Electrical Engineering Department, Ècole de Technologie Supèrieure, University of Quebec, Montreal, QC, Canada

Paulo Freitas de Araújo Filho, CIn, Universidade Federal de Pernambuco, Recife, Brazil