AuthorsE. Catak, F. O. Catak and A. Moldsvor
TitleAdversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case
AfilliationSoftware Engineering
Project(s)Department of Engineering Complex Software Systems
StatusAccepted
Publication TypeProceedings, refereed
Year of Publication2021
Conference NameIEEE International Black Sea Conference on Communications and Networking
Publisher IEEE
Place PublishedIEEE
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 when applying the algorithms. While machine learning offers significant advantages for 6G, AI models' security is normally ignored. Due to the many applications in the real world, security is a vital part of the algorithms. This paper proposes a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction using 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. We also present 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 under attack are very close to the undefended model without attack.

Citation Key27849

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