The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

by   Murat Kuzlu, et al.

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, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Momentum Iterative Method (MIM), because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study also offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence (AI)-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods effectively defend the DNN models against adversarial attacks in next-generation wireless networks.


page 17

page 18

page 19

page 21


Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case

6G is the next generation for the communication systems. In recent years...

Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training

The successful emergence of deep learning (DL) in wireless system applic...

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

6G – sixth generation – is the latest cellular technology currently unde...

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the found...

Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals

Cellular networks (LTE, 5G, and beyond) are dramatically growing with hi...

Over-The-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recen...

Please sign up or login with your details

Forgot password? Click here to reset