Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security

Mahmoud, Haitham and Ismail, Tawfik and Baiyekusi, Tobi and Idrissi, Moad (2024) Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security. Network, 4 (4). pp. 453-467. ISSN 2673-8732

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Abstract

This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks.

Item Type:	Article
Identification Number:	10.3390/network4040023
Dates:	Date Event 8 October 2024 Accepted 23 October 2024 Published Online
Uncontrolled Keywords:	physical layer security, 6G privacy, multi-stage detection, anomaly detection, machine learning
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	07 Mar 2025 15:01
Last Modified:	07 Mar 2025 15:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16210

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