2021/05/09 by Ferhat Özgür Çatak, Catak, Ferhat Ozgur, Evren Çatak +7
Biochemistry, Genetics and Molecular Biology · Computer Science · #Adversarial Robustness in Machine Learning #Bacillus and Francisella bacterial research #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Physical Unclonable Functions (PUFs) and Hardware Security #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.03905
openalex publication_date 2021/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
6G -- sixth generation -- is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous car, and many more. Those algorithms have been also using in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of machine learning techniques, especially deep learning, it is critical to take the security concern into account when applying the algorithms. While machine learning algorithms offer significant advantages 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 AI model itself can be poisoned by attackers. 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 mmWave beam prediction application with fast gradient sign method attack. The mean square errors (MSE) of the defended model under attack are very close to the undefended model without attack.