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

TL;DR

This paper addresses security vulnerabilities in 6G mmWave beam prediction models by proposing an adversarial learning mitigation method against attacks, demonstrating comparable performance to undefended models.

Contribution

It introduces a novel adversarial attack mitigation technique for 6G mmWave beam prediction models using adversarial learning and evaluates its effectiveness.

Findings

The defended model's mean square error is close to the undefended model.

The proposed mitigation method effectively counters fast gradient sign method attacks.

Security concerns are critical for deploying reliable 6G machine learning applications.

Abstract

6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account to apply the algorithms. While machine learning offers significant advantages for 6G, AI models' security is ignored. Since it has many applications in the real world, security is a vital part of the algorithms. 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. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction use case. We have also presented the adversarial learning mitigation method's performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model and undefended model are very close.