Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case
This addresses security vulnerabilities in 6G communication systems, which is an incremental improvement for ensuring reliable AI applications in real-world scenarios.
The paper tackles adversarial attacks on machine learning models used for mmWave beam prediction in 6G systems, proposing a mitigation method based on adversarial learning, with results showing that the mean square errors of defended and undefended models are very close.
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.