  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.
