The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining. (arXiv:2202.08185v1 [cs.CR])

The design of a security scheme for beamforming prediction is critical for
next-generation wireless networks (5G, 6G, and beyond). However, there is no
consensus about protecting the beamforming prediction using deep learning
algorithms in these networks. This paper presents the security vulnerabilities
in deep learning for beamforming prediction using deep neural networks (DNNs)
in 6G wireless networks, which treats the beamforming prediction as a
multi-output regression problem. It is indicated that the initial DNN model is
vulnerable against adversarial attacks, …

defensive mitigations prediction security