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6G -- sixth generation -- is the latest cellular technology currently underdevelopment for wireless communication systems. In recent years, machinelearning algorithms have been applied widely in various fields, such ashealthcare, transportation, energy, autonomous car, and many more. Thosealgorithms have been also using in communication technologies to improve thesystem performance in terms of frequency spectrum usage, latency, and security.With the rapid developments of machine learning techniques, especially deeplearning, it is critical to take the security concern into account whenapplying the algorithms. While machine learning algorithms offer significantadvantages for 6G networks, security concerns on Artificial Intelligent (AI)models is typically ignored by the scientific community so far. However,security is also a vital part of the AI algorithms, this is because the AImodel itself can be poisoned by attackers. This paper proposes a mitigationmethod for adversarial attacks against proposed 6G machine learning models forthe millimeter-wave (mmWave) beam prediction using adversarial learning. Themain idea behind adversarial attacks against machine learning models is toproduce faulty results by manipulating trained deep learning models for 6Gapplications for mmWave beam prediction. We also present the adversariallearning mitigation method's performance for 6G security in mmWave beamprediction application with fast gradient sign method attack. The mean squareerrors (MSE) of the defended model under attack are very close to theundefended model without attack.

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction