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6G is the next generation for the communication systems. In recent years,machine learning algorithms have been applied widely in various fields such ashealth, transportation, and the autonomous car. The predictive algorithms willbe used in 6G problems. With the rapid developments of deep learningtechniques, it is critical to take the security concern into account to applythe algorithms. While machine learning offers significant advantages for 6G, AImodels' security is ignored. Since it has many applications in the real world,security is a vital part of the algorithms. This paper has proposed amitigation method for adversarial attacks against proposed 6G machine learningmodels for the millimeter-wave (mmWave) beam prediction with adversariallearning. The main idea behind adversarial attacks against machine learningmodels is to produce faulty results by manipulating trained deep learningmodels for 6G applications for mmWave beam prediction use case. We have alsopresented the adversarial learning mitigation method's performance for 6Gsecurity in millimeter-wave beam prediction application with fast gradient signmethod attack. The mean square errors of the defended model and undefendedmodel are very close.

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