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Found 5 papers, 1 papers with code
Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals
Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication technologies.
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks
The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions.
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
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.
Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction
We also present the adversarial learning mitigation method's performance for 6G security in mmWave beam prediction application with fast gradient sign method attack.
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case
This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning.
