Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks

1 code implementation • 12 Aug 2022 • Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler

The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions.

Adversarial Attack

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

no code implementations • 16 Feb 2022 • Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali, Evren Catak, Ozgur Guler

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.