Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
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 hyb...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Network |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-8732/4/4/23 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846103384840470528 |
|---|---|
| author | Haitham Mahmoud Tawfik Ismail Tobi Baiyekusi Moad Idrissi |
| author_facet | Haitham Mahmoud Tawfik Ismail Tobi Baiyekusi Moad Idrissi |
| author_sort | Haitham Mahmoud |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc6cb05382244a4fb8f73a00c91f6c41 |
| institution | Kabale University |
| issn | 2673-8732 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Network |
| spelling | doaj-art-bc6cb05382244a4fb8f73a00c91f6c412024-12-27T14:43:41ZengMDPI AGNetwork2673-87322024-10-014445346710.3390/network4040023Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer SecurityHaitham Mahmoud0Tawfik Ismail1Tobi Baiyekusi2Moad Idrissi3Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B4 7RQ, UKCollege of Engineering, Taibah University, Madinah 42353, Saudi ArabiaFaculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B4 7RQ, UKSchool of Computing and Data Science, Oryx Universal College, Liverpool John Moores University, Doha P.O. Box 12253, QatarThis 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.https://www.mdpi.com/2673-8732/4/4/23physical layer security6G privacymulti-stage detectionanomaly detectionmachine learning |
| spellingShingle | Haitham Mahmoud Tawfik Ismail Tobi Baiyekusi Moad Idrissi Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security Network physical layer security 6G privacy multi-stage detection anomaly detection machine learning |
| title | Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security |
| title_full | Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security |
| title_fullStr | Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security |
| title_full_unstemmed | Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security |
| title_short | Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security |
| title_sort | advanced security framework for 6g networks integrating deep learning and physical layer security |
| topic | physical layer security 6G privacy multi-stage detection anomaly detection machine learning |
| url | https://www.mdpi.com/2673-8732/4/4/23 |
| work_keys_str_mv | AT haithammahmoud advancedsecurityframeworkfor6gnetworksintegratingdeeplearningandphysicallayersecurity AT tawfikismail advancedsecurityframeworkfor6gnetworksintegratingdeeplearningandphysicallayersecurity AT tobibaiyekusi advancedsecurityframeworkfor6gnetworksintegratingdeeplearningandphysicallayersecurity AT moadidrissi advancedsecurityframeworkfor6gnetworksintegratingdeeplearningandphysicallayersecurity |